<div class="xblock xblock-public_view xblock-public_view-vertical" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@vertical+block@5f14f4a2b1ac4898a5f245625489dfdc" data-init="VerticalStudentView" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="vertical" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<h2 class="hd hd-2 unit-title">Presentation and Scope</h2>
<div class="vert-mod">
<div class="vert vert-0" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@1a5c6a86595940a3986f20c50190d6e1">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@1a5c6a86595940a3986f20c50190d6e1" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<h3>Introduction</h3>
<p>Welcome to the prediction models workshop. This chapter contains a tutorial on prediction of medical data using tree based models, one of the most widely used modeling techniques in computational biology. We start with the most basic model, a decision tree, and work our way up to the more recent work on gradient boosting.</p>
<p></p>
</div>
</div>
<div class="vert vert-1" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@57db4dcd16b44e77ae04ffe94a430f81">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@57db4dcd16b44e77ae04ffe94a430f81" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<h3>Pre-requisites</h3>
<p>We prepared interactive material for letting you run the exercises as you read the sections. However, some pre-requisites are required.</p>
<p>Basic programming knowledge is required for the exercises in this section. The exercises for this section are available both in R and Python. However, this chapter will be explained using the material coded in Python. You may not need to install software if you prefer to run the material on <a href="https://colab.research.google.com/notebooks/intro.ipynb#recent=true">Google Colaboratory</a>.</p>
<p>In case you prefer to use R, it is important to have the software installed locally. It is recommended to install RStudio. If you don't have RStudio installed on your computer, please download it and follow instructions for correct installation.</p>
<p></p>
<p></p>
</div>
</div>
<div class="vert vert-2" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@1092c41e082d482283ef9fb4eb62d9db">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@1092c41e082d482283ef9fb4eb62d9db" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<p>Dataset</p>
<p></p>
<ul>
<li><em><a href="http://archive.ics.uci.edu/ml/datasets/Iris">Fisher's iris dataset</a></em>.</li>
</ul>
</div>
</div>
<div class="vert vert-3" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@e45d42dd6cbe4cb3828bf40f7d3455b2">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@e45d42dd6cbe4cb3828bf40f7d3455b2" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<p>References</p>
<ol><ol>
<li>Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. <a href="http://circ.ahajournals.org/content/101/23/e215.full">PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals</a>. Circulation 2000</li>
<li>W.H. Wolberg, W.N. Street, and O.L. Mangasarian. <a href="https://pubmed.ncbi.nlm.nih.gov/8168063/">Machine learning techniques to diagnose breast cancer from fine-needle aspirates</a>. Cancer Letters 77 (1994)</li>
</ol></ol>
<p></p>
<p></p>
</div>
</div>
<div class="vert vert-4" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@5aff072a3c0540b1bc4986f9d07f1958">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@5aff072a3c0540b1bc4986f9d07f1958" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<h3>Learning objectives</h3>
<ul>
<li>Understand the simplest tree based models: <em>Decision Trees</em>, to the most complex one: <em>Gradient Boosting</em>.</li>
<li>Understand the intuition behind improvements in tree models.</li>
<li>Being able to apply these models yourself.</li>
</ul>
<p></p>
</div>
</div>
<div class="vert vert-5" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@899809577a944efba83f6aafeb91ff4d">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@899809577a944efba83f6aafeb91ff4d" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<h3>Credits</h3>
<p>Workshop authors: Alistair Johnson and Miguel Armengol.</p>
<p>Adaptation: Aldo Arévalo, Louis Agha-Mir-Salim, Wesley Hung, Zoey Hou and Grigorij Schleifer.</p>
<p></p>
</div>
</div>
</div>
</div>
<div class="xblock xblock-public_view xblock-public_view-vertical" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@vertical+block@8326ebe850ca489bbb9aa536bb58b86f" data-init="VerticalStudentView" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="vertical" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<h2 class="hd hd-2 unit-title">How to Begin</h2>
<div class="vert-mod">
<div class="vert vert-0" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@c228bfad705948f1b1c4f60caf35ae08">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@c228bfad705948f1b1c4f60caf35ae08" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<p>The tutorial is hosted on the author's <a href="https://github.com/alistairewj/tree-prediction-tutorial">GitHub repository</a>.</p>
<p>You can download (clone) the repository to your local computer and open the <strong><em>trees-classification.ipynb</em></strong><strong><em> </em></strong>or you can run the same <a href="https://colab.research.google.com/github/alistairewj/tree-prediction-tutorial/blob/master/trees-classification.ipynb">notebook</a> on Google's Colaboratory platform instead of downloading the repository and running it locally on your machine. <a href="https://colab.research.google.com/notebooks/welcome.ipynb">Colab</a> provides a free runtime to execute this notebook in.</p>
<p></p>
<p>You do not need access to any databases (on your computer or on BigQuery) for this workshop. We will be alternating between sample datasets available with <a href="https://scikit-learn.org/stable/">scikit-learn</a> and a medical dataset made available in <a href="https://github.com/alistairewj/tree-prediction-tutorial/tree/master/data">author's repository</a> (and originally sourced from <a hrer="https://physionet.org/about/database/">PhysioNet</a>).</p>
<p></p>
<p><a href="https://github.com/alistairewj/tree-prediction-tutorial/blob/master/trees.Rmd">Rmarkdown File</a> (optional).</p>
<p></p>
<p>How to <a href="https://docs.github.com/en/free-pro-team@latest/github/creating-cloning-and-archiving-repositories/cloning-a-repository">clone</a> a repository. To open a Jupyter notebook, use the terminal and navigate to the directory where you saved the repository. Then type the command:</p>
<pre>jupyter notebook</pre>
</div>
</div>
<div class="vert vert-1" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@931f66b48c4e43afbf7d0dbb4133fe9d">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@931f66b48c4e43afbf7d0dbb4133fe9d" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<h3>Setting up the environment</h3>
<p>If you downloaded the repository to your local machine most requirements should be met with a simple terminal command:</p>
<pre>pip install pydotplus numpy pandas sklearn matplotlib jupyter</pre>
<p></p>
<p>If you want the exact version of all the packages installed, you can use the <em>requirements.txt</em> file from the repository:</p>
<pre>pip install -r requirements.txt</pre>
<p></p>
<div id="gtx-trans" style="position: absolute; left: 21px; top: 143.031px;">
<div class="gtx-trans-icon"></div>
</div>
</div>
</div>
</div>
</div>
<div class="xblock xblock-public_view xblock-public_view-vertical" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@vertical+block@e595430eacb74f2b91e2750023c9c916" data-init="VerticalStudentView" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="vertical" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<h2 class="hd hd-2 unit-title">Dataset: Fisher's Iris</h2>
<div class="vert-mod">
<div class="vert vert-0" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@0acf767ae20a4a3f829b14c94bbec081">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@0acf767ae20a4a3f829b14c94bbec081" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<h3>Introduction</h3>
<p style="text-align: justify;">Many sections will be explained with the aid of Fisher's iris dataset, one of the best known benchmark datasets in statistics and machine learning.</p>
<p style="text-align: justify;">This data was collected by Edgar Anderson and was used by Fisher in 1936 to demonstrate Linear Discriminant Analysis (LDA). We won't talk about LDA in this tutorial but it's an interesting technique worth learning about! The iris dataset includes 50 petal and sepal measurements for three species of flowers.</p>
</div>
</div>
<div class="vert vert-1" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@c1e75ad3fb6d43adac825e8e7183509a">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@c1e75ad3fb6d43adac825e8e7183509a" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<h3>Importing libraries and datasets</h3>
<p style="text-align: justify;">A cell is a basic unit in Jupyter Notebooks. It may contain code or text that could complement the code you want to run/show. This is pretty useful in the development of collaborative projects of data science in medicine. The code can be shown in a user-friendly manner to clinicians without a strong background in programming, but still powerful enough to bee used for sophisticated data analysis.</p>
<p style="text-align: justify;">The text cells are formatted using a simple markup language called markdown. This <a href="https://colab.research.google.com/notebooks/markdown_guide.ipynb">guide</a> would help you personalize your text cells. To edit an existing one, just double click on each one.</p>
<p style="text-align: justify;">To run a cell containing code, you can either click on the play button display on the upper left corner of each cell as shown below, or any of the shortcuts <span style="font-family: 'courier new', courier;">cmd/ctrl + shift + enter</span> keys. The below cell installs necessary packages if running this notebook from Google Colaboratory.</p>
<p><img src="data:image/png;base64,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" alt="" /></p>
<p style="text-align: justify;">This tutorial is meant to be an introduction to tree based methods for prediction. The following cell is going to call the sample datasets available with scikit-learn and other libraries needed to run the notebook.</p>
<p style="text-align: center;"><img height="469" width="800" src="/assets/courseware/v1/cd2976b34210f2c8b53a740bf585d8e8/asset-v1:MITx+HST.953x+3T2020+type@asset+block/Captura_de_Pantalla_2020-02-18_a_la_s__10.02.08_a._m..png" alt="Importing libraries" /></p>
</div>
</div>
<div class="vert vert-2" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@f79f79713dfb4c27a3269e5f10bba056">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@f79f79713dfb4c27a3269e5f10bba056" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<h3>Helper functions</h3>
<p style="text-align: justify;">Below we define a helper function (<span style="font-family: 'courier new', courier;">plot_model_pred_2d</span>) which will help us plot (<strong>i</strong>) the decision that the tree makes as the background color and (<strong>ii</strong>) the actual data colored with their true outcome. So, if blue overlaps with blue on our plot, we can be fairly happy that the algorithm is working well.</p>
<p style="text-align: justify;"><img src="data:image/png;base64,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" alt="" /></p>
<p style="text-align: justify;">Note that while there are more rigorous and quantitative forms of evaluating models, one can gain a lot of insight from visualizing the data. In order to make the colormaps have the same color as the decision tree, we use a <span style="font-family: 'courier new', courier;">make_colormap</span> helper function.</p>
<p style="text-align: justify;"><img src="data:image/png;base64,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" alt="" /></p>
<!--
<p style="text-align: center;"><img height="548" width="800" src="/assets/courseware/v1/6d59901b1b40d6a92d74ee1e032b11f7/asset-v1:MITx+HST.953x+3T2020+type@asset+block/Captura_de_Pantalla_2020-02-18_a_la_s__11.59.49_a._m..png" alt="Helper functions" /></p>
-->
<p style="text-align: justify;">Helper functions will help us to run repetitive tasks along with the script and should be defined before they are called in the code.</p>
<p></p>
</div>
</div>
<div class="vert vert-3" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@c9e94030c3344fc4af4ffeea44fa8fd6">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@c9e94030c3344fc4af4ffeea44fa8fd6" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<h3>Loading data</h3>
<p>The below code:</p>
<ul>
<li>Loads in the dataset</li>
<li>Prints out a brief description</li>
<li>Extracts two columns of the data into <em>X.</em></li>
<li>Extracts the class labels into <em>y</em>.</li>
</ul>
<p style="text-align: justify;">Note that we only use two columns of data because we'd like to visualize the classifier. Also, we only use data from index 50 onward because we'd like to focus on two plants: versicolour and virginica.</p>
<p style="text-align: justify;"><img src="data:image/png;base64,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" alt="" /></p>
<p></p>
<p></p>
</div>
</div>
</div>
</div>
<div class="xblock xblock-public_view xblock-public_view-vertical" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@vertical+block@1f50c295aa914eb09196b3ca513484f6" data-init="VerticalStudentView" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="vertical" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<h2 class="hd hd-2 unit-title">Decision Trees</h2>
<div class="vert-mod">
<div class="vert vert-0" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@501d6b2d9c2b4427b693d0ea72f98d27">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@501d6b2d9c2b4427b693d0ea72f98d27" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<h3>Background</h3>
<p style="text-align: justify;">Let's build the simplest tree model we can think of: a classification tree with only one split. Decision trees of this form are commonly referred to under the umbrella term Classification and Regression Trees (CART) [1]. While we will only be looking at classification here, regression isn't too different. After grouping the data (which is essentially what a decision tree does), classification involves assigning all members of the group to the majority class (category or tag) of that group during training. Regression is the same, except you would assign the average value (continuous value), not the majority.</p>
<p></p>
<p style="text-align: justify;">In the case of a decision tree with one split, often called a "<em>stump</em>", the model will partition the data into two groups, and assign classes for those two groups based on majority vote. There are many parameters available for the <span style="font-family: 'courier new', courier;">DecisionTreeClassifier</span> class; by specifying <span style="font-family: 'courier new', courier;">max_depth=1</span> we will build a decision tree with only one split - i.e. of depth 1.</p>
<p style="text-align: justify;">[1] L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Wadsworth, Belmont, CA, 1984.</p>
</div>
</div>
<div class="vert vert-1" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@1e0ec286ba994332b290635570556485">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@1e0ec286ba994332b290635570556485" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<h3>Fitting the model</h3>
<p style="text-align: justify;">The cell below calls the class <span style="font-family: 'courier new', courier;"><a href="https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html">DecisionTreeClassifier</a></span>. It would help us to tailor our decision tree model with properties such as <span style="font-family: 'courier new', courier;">max_depth</span> set to 1 in our example.</p>
<p style="text-align: center;"><img height="101" width="800" src="/assets/courseware/v1/e7f598eb9df9c67e723526ef64a3b1ed/asset-v1:MITx+HST.953x+3T2020+type@asset+block/Captura_de_Pantalla_2020-02-18_a_la_s__12.21.44_p._m..png" alt="Fitting model" /></p>
<p>Since our model is so simple, we can actually look at the full decision tree.</p>
<p style="text-align: center;"><img height="263" width="800" src="/assets/courseware/v1/bc42a05b5e103c74a56f24f99598e9c3/asset-v1:MITx+HST.953x+3T2020+type@asset+block/Captura_de_Pantalla_2020-02-18_a_la_s__12.22.18_p._m..png" alt="Tree 1" /></p>
<p>Here we see three nodes: a white node at the top, an orange node in the lower left, and a blue node in the lower right. The top (white) node is the <strong>root</strong> of the tree: it contains all the data. Let's read this node bottom to top, starting with <span style="font-family: 'courier new', courier;">value = [50, 50]</span>. This line is telling us the current class balance: there are 50 observations of class <strong>1</strong>, and 50 observations of class <strong>2</strong>. In our iris data, that translates to 50 versicolour and 50 virginica. The designation of tags or classes is up to every analyst. Some analysts may call them class 0 and 1.</p>
<p>Moving up a line, <span style="font-family: 'courier new', courier;">samples = 100</span> reminds us how many rows of data are assessed at this node. Since it's the root of our tree, all the data is assessed. Moving up again we have <span style="font-family: 'courier new', courier;">gini = 0.5</span>. This is the <em>Gini index</em>, and is very important as it is the value that is used to split data. The <em>Gini index</em> is a measure of impurity - the higher the value is the bigger mix of classes that you have. Right now we have a 50/50 split of two classes, and as a result, the gini index is <span style="font-family: 'courier new', courier;">0.5</span>: about as bad as we can do. Ideally, we'd want this value to be <span style="font-family: 'courier new', courier;">0</span>, which would indicate that only one class of data is present in that node.</p>
<p>Finally, the top line in the white node is the decision rule that has been learned for that node. That is, this line indicates what value the node will split the data on. In this case, we've moved observations with petal <span style="font-family: 'courier new', courier;">length <= 4.75 cm</span> to the left node, and implicitly all observations with petal length > 4.75 cm are moved into the right node. Looking at the two nodes, we can also see that the value for gini is lower in both, indicating that these nodes are more homogeneous. Looking at the value line, we can see that the left node has 44 observations in class <strong>1</strong> and 1 observation in class <strong>2</strong>. This is much better than the 50/50 split we had earlier!</p>
<p>It's nice to look at the gini coefficient because that's actually what the algorithm uses to determine a split. It evaluates every single feature (petal length, etc) at every possible split (4.75, 4.76, ...) to find the split which minimizes the gini in the two resultant nodes. Evaluating every possibility at each iteration and picking the best one is called a greedy approach.</p>
<div id="gtx-trans" style="position: absolute; left: 647px; top: 790.59px;">
<div class="gtx-trans-icon"></div>
</div>
</div>
</div>
<div class="vert vert-2" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@4954318f2b03456eb9549e3800815982">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@4954318f2b03456eb9549e3800815982" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<h3>Decision boundary</h3>
<p>Let's take a look at what this decision boundary actually looks like in a scatter plot.</p>
<p style="text-align: center;"><img height="564" width="800" src="/assets/courseware/v1/20ecb2ee28641c70fd6e94eb76f5ffe2/asset-v1:MITx+HST.953x+3T2020+type@asset+block/Captura_de_Pantalla_2020-02-18_a_la_s__12.48.22_p._m..png" alt="Decision boundary" /></p>
<p>Above we can see the decision boundary of 4.75 cm for petal length (y-axis). We can also see a blue circle on the far left - the 1 point we misclassified as class <strong>2</strong> which had petal length < 4.75cm. Of course, we are using a very simple model - let's see what happens when we increase the depth to 5 in the following cells of the provided Jupyter Notebook.</p>
</div>
</div>
<div class="vert vert-3" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@7ae37a55ef1a41cea532c3f738019e72">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@7ae37a55ef1a41cea532c3f738019e72" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<p>Tree pruning</p>
<p style="text-align: justify;">After running some cells in the Jupyter Notebook, you should have reached a more complex decision tree. Basically, we were only minimizing <em>Gini index</em> in each split until it reaches 0. Here is where we introduce pruning concept. It is done by setting <span style="font-family: 'courier new', courier;">min_samples_leaf=10</span>.</p>
<p style="text-align: justify;">In the next two cells we do that:</p>
<p style="text-align: center;"><img height="429" width="800" src="/assets/courseware/v1/d0534d033afcd706d611400c5f178c1a/asset-v1:MITx+HST.953x+3T2020+type@asset+block/Captura_de_Pantalla_2020-02-18_a_la_s__1.04.26_p._m..png" alt="Pruning" /></p>
<p style="text-align: justify;">After the second cell we should be able to render a simpler but still well delimiting model:</p>
<p style="text-align: center;"><img height="445" width="800" src="/assets/courseware/v1/8bc52f221bb0a0e34056f4bc8e18cce3/asset-v1:MITx+HST.953x+3T2020+type@asset+block/Captura_de_Pantalla_2020-02-18_a_la_s__1.07.37_p._m..png" alt="Decision diagram 2" /></p>
<p style="text-align: justify;">Above, we can see that our second tree is (<strong>1</strong>) smaller in-depth, and (<strong>2</strong>) never splits a node with <= 10 samples. We can look at the decision surface (scatterplot) for this tree in the next cells in your notebook.</p>
</div>
</div>
<div class="vert vert-4" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@fe6189f54a484d8d836305e55a644976">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@fe6189f54a484d8d836305e55a644976" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<h3>High variance vs simplicity</h3>
<p>Previously, we reduced our performance in an effort to simplify the tree. This is the classic machine learning problem of trading off complexity with error.</p>
<p>If you remember, we set the minimum samples per leaf node of 10. Why 10? Why not 5? Why not 20? The answer is: it depends on the dataset. Heuristically choosing these parameters can be time-consuming, and we will see later on how gradient boosting elegantly handles this task.</p>
<p>Before we move on to boosting, it will be useful to demonstrate how decision trees have high "variance". In this context, variance refers to a property of some models to have a wide range of performance given random samples of data. Let's take a look at randomly slicing the data we have to see what that means.</p>
<p style="text-align: center;"><img height="178" width="600" src="/assets/courseware/v1/4e7aa8dfae284079b75ea830d8b4bdf0/asset-v1:MITx+HST.953x+3T2020+type@asset+block/Captura_de_Pantalla_2020-02-18_a_la_s__2.41.46_p._m..png" alt="High variance" /></p>
<p>Above we can see that we are using random subsets of data (for reproducibility, <span style="font-family: 'courier new', courier;">seed</span> was set to <span style="font-family: 'courier new', courier;">123</span>), and as a result, our decision boundary can change quite a bit. As you could guess, we actually don't want a model that randomly works well and randomly works poorly, so you may wonder why this is useful. The trick is that by combining many of instances of "high variance" classifiers (decision trees), we can end up with a single classifier with low variance. That is the principle of ensemble models we are going to explore in this workshop.</p>
</div>
</div>
<div class="vert vert-5" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@52c8c845475c48819697b3bbdb934847">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@52c8c845475c48819697b3bbdb934847" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<h3>Properties summary</h3>
<p>To wrap up, tree properties to be tuned are:</p>
<ul>
<li><strong>Splitting criteria</strong></li>
<ul>
<li>Gini index and entropy</li>
</ul>
<li><strong>Limit the tree size</strong></li>
<ul>
<li>Maximum tree depth and prune the tree</li>
</ul>
<li><strong>Number of samples</strong></li>
<ul>
<li>Minimum in a leaf</li>
<li>Minimum to split in a node</li>
</ul>
<li><strong>Software package</strong></li>
<ul>
<li>CART</li>
<li>ID3</li>
<li>C4.5</li>
<li>C5.0 (computationally more expensive)</li>
</ul>
</ul>
</div>
</div>
</div>
</div>
<div class="xblock xblock-public_view xblock-public_view-vertical" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@vertical+block@b37e1a06d24d45ceb8f1da7f315f7241" data-init="VerticalStudentView" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="vertical" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<h2 class="hd hd-2 unit-title">Boosting</h2>
<div class="vert-mod">
<div class="vert vert-0" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@13f5c6073ecf4049ab734407fd43a07f">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@13f5c6073ecf4049ab734407fd43a07f" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<h3>Introduction</h3>
<p style="text-align: justify;">The premise of boosting is the combination of many weak learners to form a single "strong" learner. In a nutshell, boosting involves building models iteratively, and at each step, focus on the data we performed poorly on.</p>
<p style="text-align: justify;"></p>
<p></p>
</div>
</div>
<div class="vert vert-1" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@14cbd407705544239a410e50d89bf308">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@14cbd407705544239a410e50d89bf308" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<h3>Build the model</h3>
<p style="text-align: justify;">In our context, we'll use decision trees. The first step would be to build a tree using the data. Next, we'd look at the data that we misclassified and re-weight the data to classify those observations correctly, at a cost of maybe getting some of the other data wrong this time. Let's see how this works in practice.</p>
<p style="text-align: center;"><img height="500" width="712" src="/assets/courseware/v1/a70fbaa6e80332361ed696fb967d7a8b/asset-v1:MITx+HST.953x+3T2020+type@asset+block/Screen_Shot_2020-02-19_at_2.17.55_AM.png" alt="b1" /></p>
<p style="text-align: justify;">Looking at the above, we can see that the first iteration builds the exact same simple decision tree as we had seen earlier. This makes sense - it's using the entire dataset with no special weighting.</p>
<p style="text-align: justify;">In the next iteration, we can see the model shift - it misclassified five observations in class 1, and now these are the most important observations. Consequently, it picks the boundary in such a way that, while prioritizing correctly classified observations, the model still tries to classify the rest of the data too. Now we have correctly classified all but one observation, the one on the far left middle of the graph. In iteration 3, the algorithm solely focuses on correctly classifying this one observation.</p>
<p style="text-align: justify;">One important point is that each tree is weighted by its a global error. In the figure above, it's obvious that we wouldn't want to weight Tree 3 equally to Tree 1 when Tree 1 is doing so much better overall. It turns out that weighting each tree by the inverse of its error is a pretty good way to do this.</p>
</div>
</div>
<div class="vert vert-2" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@f7f633ae80314bd3ab9c7bd542ee7b64">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@f7f633ae80314bd3ab9c7bd542ee7b64" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<h3>Final model's decision surface</h3>
<p style="text-align: center;"><img height="400" width="819" src="/assets/courseware/v1/73197aae2460e0d547f9a1b8595b4994/asset-v1:MITx+HST.953x+3T2020+type@asset+block/Screen_Shot_2020-02-19_at_2.25.53_AM.png" alt="b2" /></p>
<p style="text-align: justify;">And that's AdaBoost! There are a few tricks we have glossed over here - but you understand the general principle. Now we'll move on to a different approach. With boosting, we iteratively changed the dataset to have new trees focus on the "difficult" observations. The next approach we discuss is similar as it also involves using changed versions of our dataset to build new trees.</p>
</div>
</div>
</div>
</div>
<div class="xblock xblock-public_view xblock-public_view-vertical" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@vertical+block@3b598943d99e4204a589d9d7e192c0fe" data-init="VerticalStudentView" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="vertical" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<h2 class="hd hd-2 unit-title">Bagging</h2>
<div class="vert-mod">
<div class="vert vert-0" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@19730c9ceae248ca94d11ee40f691e7b">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@19730c9ceae248ca94d11ee40f691e7b" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<h4>Introduction</h4>
<p>In this section, we are going to explore another type of <em>ensemble learning</em> that aims to build a unique model by combining many models together. Bringing back to our memories, AdaBoost is an algorithm that modifies data to focus on entries that are hard to classify. It could be also named as resampling the data for each new tree where we increase the importance of misclassifications.</p>
<p>Bagging is almost the same, except that we don't selectively choose which observations to focus on, but rather we randomly select subsets of data each time or in each iteration. Whereas AdaBoost aims to iteratively improve our overall model with new trees, we now build trees on what we hope are independent datasets.</p>
</div>
</div>
<div class="vert vert-1" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@e5372387c5414c29a2657606f79c516f">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@e5372387c5414c29a2657606f79c516f" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<h4>Bagging exercise</h4>
<p>Let's build a decision tree classifier to understand a simple bootstrap model with the iris dataset.</p>
<p style="text-align: center;"><img height="500" width="712" src="/assets/courseware/v1/b8199420bb6ca49f2725fb5e6eab3a69/asset-v1:MITx+HST.953x+3T2020+type@asset+block/Screenshot_2020-05-26_at_13.47.59.png" alt="Results after running the code that builds a decision tree classifier." /></p>
<p>We can observe from the plots that each individual tree is quite variable - this is a result of using a random set of data to train the classifier.</p>
<p style="text-align: center;"><img height="350" width="612" src="/assets/courseware/v1/c342cb95dac54e80b14a0d971fddfe98/asset-v1:MITx+HST.953x+3T2020+type@asset+block/Screenshot_2020-05-26_at_13.55.33.png" alt="Plot for the final model" /></p>
<p>After checking there is high variance, the final result is quite good. Also because this is a simple dataset.</p>
</div>
</div>
<div class="vert vert-2" data-id="block-v1:MITx+HST.953x+3T2020+type@problem+block@1933c32541b141a3903996b455826280">
<div class="xblock xblock-public_view xblock-public_view-problem xmodule_display xmodule_ProblemBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@problem+block@1933c32541b141a3903996b455826280" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="problem" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="True" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "Problem"}
</script>
<div id="problem_1933c32541b141a3903996b455826280" class="problems-wrapper" role="group"
aria-labelledby="1933c32541b141a3903996b455826280-problem-title"
data-problem-id="block-v1:MITx+HST.953x+3T2020+type@problem+block@1933c32541b141a3903996b455826280" data-url="/courses/course-v1:MITx+HST.953x+3T2020/xblock/block-v1:MITx+HST.953x+3T2020+type@problem+block@1933c32541b141a3903996b455826280/handler/xmodule_handler"
data-problem-score="0"
data-problem-total-possible="3"
data-attempts-used="0"
data-content="
<h3 class="hd hd-3 problem-header" id="1933c32541b141a3903996b455826280-problem-title" aria-describedby="block-v1:MITx+HST.953x+3T2020+type@problem+block@1933c32541b141a3903996b455826280-problem-progress" tabindex="-1">
Quiz: Bagging
</h3>
<div class="problem-progress" id="block-v1:MITx+HST.953x+3T2020+type@problem+block@1933c32541b141a3903996b455826280-problem-progress"></div>
<div class="problem">
<div>
<p>Let's evaluate how much you have learned so far.</p>
<div class="wrapper-problem-response" tabindex="-1" aria-label="Question 1" role="group"><div class="choicegroup capa_inputtype" id="inputtype_1933c32541b141a3903996b455826280_2_1">
<fieldset aria-describedby="status_1933c32541b141a3903996b455826280_2_1">
<legend id="1933c32541b141a3903996b455826280_2_1-legend" class="response-fieldset-legend field-group-hd">Not limiting the growth of a decision tree may lead to ...</legend>
<div class="field">
<input type="radio" name="input_1933c32541b141a3903996b455826280_2_1" id="input_1933c32541b141a3903996b455826280_2_1_choice_0" class="field-input input-radio" value="choice_0"/><label id="1933c32541b141a3903996b455826280_2_1-choice_0-label" for="input_1933c32541b141a3903996b455826280_2_1_choice_0" class="response-label field-label label-inline" aria-describedby="status_1933c32541b141a3903996b455826280_2_1"> an increased variance in our model.
</label>
</div>
<div class="field">
<input type="radio" name="input_1933c32541b141a3903996b455826280_2_1" id="input_1933c32541b141a3903996b455826280_2_1_choice_1" class="field-input input-radio" value="choice_1"/><label id="1933c32541b141a3903996b455826280_2_1-choice_1-label" for="input_1933c32541b141a3903996b455826280_2_1_choice_1" class="response-label field-label label-inline" aria-describedby="status_1933c32541b141a3903996b455826280_2_1"> overfitting.
</label>
</div>
<div class="field">
<input type="radio" name="input_1933c32541b141a3903996b455826280_2_1" id="input_1933c32541b141a3903996b455826280_2_1_choice_2" class="field-input input-radio" value="choice_2"/><label id="1933c32541b141a3903996b455826280_2_1-choice_2-label" for="input_1933c32541b141a3903996b455826280_2_1_choice_2" class="response-label field-label label-inline" aria-describedby="status_1933c32541b141a3903996b455826280_2_1"> obtain less node in each splitting.
</label>
</div>
<span id="answer_1933c32541b141a3903996b455826280_2_1"/>
</fieldset>
<div class="indicator-container">
<span class="status unanswered" id="status_1933c32541b141a3903996b455826280_2_1" data-tooltip="Not yet answered.">
<span class="sr">unanswered</span><span class="status-icon" aria-hidden="true"/>
</span>
</div>
</div></div>
<div class="wrapper-problem-response" tabindex="-1" aria-label="Question 2" role="group"><div class="choicegroup capa_inputtype" id="inputtype_1933c32541b141a3903996b455826280_3_1">
<fieldset aria-describedby="status_1933c32541b141a3903996b455826280_3_1">
<legend id="1933c32541b141a3903996b455826280_3_1-legend" class="response-fieldset-legend field-group-hd">This parameter in the class "DecisionTreeClassifier" helps us to control the growth in a decision tree from the root node to the ends at its leaves. Also helps to prevent overfitting.</legend>
<div class="field">
<input type="radio" name="input_1933c32541b141a3903996b455826280_3_1" id="input_1933c32541b141a3903996b455826280_3_1_choice_0" class="field-input input-radio" value="choice_0"/><label id="1933c32541b141a3903996b455826280_3_1-choice_0-label" for="input_1933c32541b141a3903996b455826280_3_1_choice_0" class="response-label field-label label-inline" aria-describedby="status_1933c32541b141a3903996b455826280_3_1"> min_samples_split.
</label>
</div>
<div class="field">
<input type="radio" name="input_1933c32541b141a3903996b455826280_3_1" id="input_1933c32541b141a3903996b455826280_3_1_choice_1" class="field-input input-radio" value="choice_1"/><label id="1933c32541b141a3903996b455826280_3_1-choice_1-label" for="input_1933c32541b141a3903996b455826280_3_1_choice_1" class="response-label field-label label-inline" aria-describedby="status_1933c32541b141a3903996b455826280_3_1"> min_samples_leaf.
</label>
</div>
<div class="field">
<input type="radio" name="input_1933c32541b141a3903996b455826280_3_1" id="input_1933c32541b141a3903996b455826280_3_1_choice_2" class="field-input input-radio" value="choice_2"/><label id="1933c32541b141a3903996b455826280_3_1-choice_2-label" for="input_1933c32541b141a3903996b455826280_3_1_choice_2" class="response-label field-label label-inline" aria-describedby="status_1933c32541b141a3903996b455826280_3_1"> max_depth.
</label>
</div>
<span id="answer_1933c32541b141a3903996b455826280_3_1"/>
</fieldset>
<div class="indicator-container">
<span class="status unanswered" id="status_1933c32541b141a3903996b455826280_3_1" data-tooltip="Not yet answered.">
<span class="sr">unanswered</span><span class="status-icon" aria-hidden="true"/>
</span>
</div>
</div></div>
<div class="wrapper-problem-response" tabindex="-1" aria-label="Question 3" role="group"><div class="choicegroup capa_inputtype" id="inputtype_1933c32541b141a3903996b455826280_4_1">
<fieldset aria-describedby="status_1933c32541b141a3903996b455826280_4_1">
<legend id="1933c32541b141a3903996b455826280_4_1-legend" class="response-fieldset-legend field-group-hd">This parameter in the class "DecisionTreeClassifier" helps us to control the number of samples a node must contain in order to consider splitting.</legend>
<div class="field">
<input type="radio" name="input_1933c32541b141a3903996b455826280_4_1" id="input_1933c32541b141a3903996b455826280_4_1_choice_0" class="field-input input-radio" value="choice_0"/><label id="1933c32541b141a3903996b455826280_4_1-choice_0-label" for="input_1933c32541b141a3903996b455826280_4_1_choice_0" class="response-label field-label label-inline" aria-describedby="status_1933c32541b141a3903996b455826280_4_1"> min_samples_split.
</label>
</div>
<div class="field">
<input type="radio" name="input_1933c32541b141a3903996b455826280_4_1" id="input_1933c32541b141a3903996b455826280_4_1_choice_1" class="field-input input-radio" value="choice_1"/><label id="1933c32541b141a3903996b455826280_4_1-choice_1-label" for="input_1933c32541b141a3903996b455826280_4_1_choice_1" class="response-label field-label label-inline" aria-describedby="status_1933c32541b141a3903996b455826280_4_1"> min_samples_leaf.
</label>
</div>
<div class="field">
<input type="radio" name="input_1933c32541b141a3903996b455826280_4_1" id="input_1933c32541b141a3903996b455826280_4_1_choice_2" class="field-input input-radio" value="choice_2"/><label id="1933c32541b141a3903996b455826280_4_1-choice_2-label" for="input_1933c32541b141a3903996b455826280_4_1_choice_2" class="response-label field-label label-inline" aria-describedby="status_1933c32541b141a3903996b455826280_4_1"> max_depth.
</label>
</div>
<span id="answer_1933c32541b141a3903996b455826280_4_1"/>
</fieldset>
<div class="indicator-container">
<span class="status unanswered" id="status_1933c32541b141a3903996b455826280_4_1" data-tooltip="Not yet answered.">
<span class="sr">unanswered</span><span class="status-icon" aria-hidden="true"/>
</span>
</div>
</div></div>
</div>
<div class="action">
<input type="hidden" name="problem_id" value="Quiz: Bagging" />
<div class="submit-attempt-container">
<button type="button" class="submit btn-brand" data-submitting="Submitting" data-value="Submit" data-should-enable-submit-button="True" aria-describedby="submission_feedback_1933c32541b141a3903996b455826280" >
<span class="submit-label">Submit</span>
</button>
<div class="submission-feedback" id="submission_feedback_1933c32541b141a3903996b455826280">
<span class="sr">Some problems have options such as save, reset, hints, or show answer. These options follow the Submit button.</span>
</div>
</div>
<div class="problem-action-buttons-wrapper">
</div>
</div>
<div class="notification warning notification-gentle-alert
is-hidden"
tabindex="-1">
<span class="icon fa fa-exclamation-circle" aria-hidden="true"></span>
<span class="notification-message" aria-describedby="1933c32541b141a3903996b455826280-problem-title">
</span>
<div class="notification-btn-wrapper">
<button type="button" class="btn btn-default btn-small notification-btn review-btn sr">Review</button>
</div>
</div>
<div class="notification warning notification-save
is-hidden"
tabindex="-1">
<span class="icon fa fa-save" aria-hidden="true"></span>
<span class="notification-message" aria-describedby="1933c32541b141a3903996b455826280-problem-title">None
</span>
<div class="notification-btn-wrapper">
<button type="button" class="btn btn-default btn-small notification-btn review-btn sr">Review</button>
</div>
</div>
<div class="notification general notification-show-answer
is-hidden"
tabindex="-1">
<span class="icon fa fa-info-circle" aria-hidden="true"></span>
<span class="notification-message" aria-describedby="1933c32541b141a3903996b455826280-problem-title">Answers are displayed within the problem
</span>
<div class="notification-btn-wrapper">
<button type="button" class="btn btn-default btn-small notification-btn review-btn sr">Review</button>
</div>
</div>
</div>
"
data-graded="False">
<p class="loading-spinner">
<i class="fa fa-spinner fa-pulse fa-2x fa-fw"></i>
<span class="sr">Loading…</span>
</p>
</div>
</div>
</div>
</div>
</div>
<div class="xblock xblock-public_view xblock-public_view-vertical" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@vertical+block@48fd6ef0488941e999313c47bf55bee6" data-init="VerticalStudentView" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="vertical" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<h2 class="hd hd-2 unit-title">Random Forest</h2>
<div class="vert-mod">
<div class="vert vert-0" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@46b374f4da9c4e3fb771ffa05560d5a8">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@46b374f4da9c4e3fb771ffa05560d5a8" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<p>Before, we used bagging to randomly resample our data to generate "new" datasets to build trees from. The Random Forest takes this one step further: instead of just resampling our data, we also select only a fraction of the features to include. It turns out that this subselection tends to improve the performance of our models. The odds of an individual being very good or very bad is higher (i.e. the variance of the trees is increased), and this ends up giving us a final model with better overall performance (lower bias).</p>
</div>
</div>
<div class="vert vert-1" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@2309d3df5ff34f90b3c22e7e55ebf75a">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@2309d3df5ff34f90b3c22e7e55ebf75a" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<p><span style="color: rgb(49, 49, 49); font-family: "Open Sans", "Helvetica Neue", Helvetica, Arial, sans-serif;">The following python code block builds a random forest model using the sklearn package.</span></p>
<pre style="margin-top: 0px; margin-bottom: 0px; padding: 0px; font-family: monospace; font-size: 14px; color: rgb(51, 51, 51); border-radius: 4px; line-height: inherit; word-break: break-all; background-color: rgb(247, 247, 247); border: none;"><span class="n" style="margin: 0px; padding: 0px;">np</span><span class="o" style="margin: 0px; padding: 0px; color: rgb(102, 102, 102);">.</span><span class="n" style="margin: 0px; padding: 0px;">random</span><span class="o" style="margin: 0px; padding: 0px; color: rgb(102, 102, 102);">.</span><span class="n" style="margin: 0px; padding: 0px;">seed</span><span class="p" style="margin: 0px; padding: 0px;">(</span><span class="mi" style="margin: 0px; padding: 0px; color: rgb(102, 102, 102);">321</span><span class="p" style="margin: 0px; padding: 0px;">)</span>
<span class="n" style="margin: 0px; padding: 0px;">mdl</span> <span class="o" style="margin: 0px; padding: 0px; color: rgb(102, 102, 102);">=</span> <span class="n" style="margin: 0px; padding: 0px;">ensemble</span><span class="o" style="margin: 0px; padding: 0px; color: rgb(102, 102, 102);">.</span><span class="n" style="margin: 0px; padding: 0px;">RandomForestClassifier</span><span class="p" style="margin: 0px; padding: 0px;">(</span><span class="n" style="margin: 0px; padding: 0px;">max_depth</span><span class="o" style="margin: 0px; padding: 0px; color: rgb(102, 102, 102);">=</span><span class="mi" style="margin: 0px; padding: 0px; color: rgb(102, 102, 102);">5</span><span class="p" style="margin: 0px; padding: 0px;">,</span> <span class="n" style="margin: 0px; padding: 0px;">n_estimators</span><span class="o" style="margin: 0px; padding: 0px; color: rgb(102, 102, 102);">=</span><span class="mi" style="margin: 0px; padding: 0px; color: rgb(102, 102, 102);">6</span><span class="p" style="margin: 0px; padding: 0px;">,</span> <span class="n" style="margin: 0px; padding: 0px;">max_features</span><span class="o" style="margin: 0px; padding: 0px; color: rgb(102, 102, 102);">=</span><span class="mi" style="margin: 0px; padding: 0px; color: rgb(102, 102, 102);">1</span><span class="p" style="margin: 0px; padding: 0px;">)</span>
<span class="n" style="margin: 0px; padding: 0px;">mdl</span> <span class="o" style="margin: 0px; padding: 0px; color: rgb(102, 102, 102);">=</span> <span class="n" style="margin: 0px; padding: 0px;">mdl</span><span class="o" style="margin: 0px; padding: 0px; color: rgb(102, 102, 102);">.</span><span class="n" style="margin: 0px; padding: 0px;">fit</span><span class="p" style="margin: 0px; padding: 0px;">(</span><span class="n" style="margin: 0px; padding: 0px;">X</span><span class="p" style="margin: 0px; padding: 0px;">,</span><span class="n" style="margin: 0px; padding: 0px;">y</span><span class="p" style="margin: 0px; padding: 0px;">)</span></pre>
</div>
</div>
<div class="vert vert-2" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@0ab8c7b63f7b406eaa0968a848288e14">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@0ab8c7b63f7b406eaa0968a848288e14" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<p>Use the following code block to visualize the trees.</p>
<pre style="margin-top: 0px; margin-bottom: 0px; padding: 0px; font-family: monospace; font-size: 14px; color: #333333; border-radius: 4px; line-height: inherit; word-break: break-all; border: none;"><span class="n" style="margin: 0px; padding: 0px;">fig</span> <span class="o" style="margin: 0px; padding: 0px; color: #666666;">=</span> <span class="n" style="margin: 0px; padding: 0px;">plt</span><span class="o" style="margin: 0px; padding: 0px; color: #666666;">.</span><span class="n" style="margin: 0px; padding: 0px;">figure</span><span class="p" style="margin: 0px; padding: 0px;">(</span><span class="n" style="margin: 0px; padding: 0px;">figsize</span><span class="o" style="margin: 0px; padding: 0px; color: #666666;">=</span><span class="p" style="margin: 0px; padding: 0px;">[</span><span class="mi" style="margin: 0px; padding: 0px; color: #666666;">12</span><span class="p" style="margin: 0px; padding: 0px;">,</span><span class="mi" style="margin: 0px; padding: 0px; color: #666666;">8</span><span class="p" style="margin: 0px; padding: 0px;">])</span>
<span class="k" style="margin: 0px; padding: 0px; color: #008000; font-weight: bold;">for</span> <span class="n" style="margin: 0px; padding: 0px;">i</span><span class="p" style="margin: 0px; padding: 0px;">,</span> <span class="n" style="margin: 0px; padding: 0px;">estimator</span> <span class="ow" style="margin: 0px; padding: 0px; color: #aa22ff; font-weight: bold;">in</span> <span class="nb" style="margin: 0px; padding: 0px; color: green;">enumerate</span><span class="p" style="margin: 0px; padding: 0px;">(</span><span class="n" style="margin: 0px; padding: 0px;">mdl</span><span class="o" style="margin: 0px; padding: 0px; color: #666666;">.</span><span class="n" style="margin: 0px; padding: 0px;">estimators_</span><span class="p" style="margin: 0px; padding: 0px;">):</span>
<span class="n" style="margin: 0px; padding: 0px;">xlabel</span> <span class="o" style="margin: 0px; padding: 0px; color: #666666;">=</span> <span class="n" style="margin: 0px; padding: 0px;">feat</span><span class="p" style="margin: 0px; padding: 0px;">[</span><span class="mi" style="margin: 0px; padding: 0px; color: #666666;">0</span><span class="p" style="margin: 0px; padding: 0px;">]</span> <span class="k" style="margin: 0px; padding: 0px; color: #008000; font-weight: bold;">if</span> <span class="n" style="margin: 0px; padding: 0px;">i</span> <span class="o" style="margin: 0px; padding: 0px; color: #666666;">==</span> <span class="mi" style="margin: 0px; padding: 0px; color: #666666;">4</span> <span class="k" style="margin: 0px; padding: 0px; color: #008000; font-weight: bold;">else</span> <span class="kc" style="margin: 0px; padding: 0px; color: #008000; font-weight: bold;">None</span>
<span class="n" style="margin: 0px; padding: 0px;">ylabel</span> <span class="o" style="margin: 0px; padding: 0px; color: #666666;">=</span> <span class="n" style="margin: 0px; padding: 0px;">feat</span><span class="p" style="margin: 0px; padding: 0px;">[</span><span class="mi" style="margin: 0px; padding: 0px; color: #666666;">1</span><span class="p" style="margin: 0px; padding: 0px;">]</span> <span class="k" style="margin: 0px; padding: 0px; color: #008000; font-weight: bold;">if</span> <span class="n" style="margin: 0px; padding: 0px;">i</span> <span class="o" style="margin: 0px; padding: 0px; color: #666666;">%</span> <span class="mi" style="margin: 0px; padding: 0px; color: #666666;">3</span> <span class="o" style="margin: 0px; padding: 0px; color: #666666;">==</span> <span class="mi" style="margin: 0px; padding: 0px; color: #666666;">0</span> <span class="k" style="margin: 0px; padding: 0px; color: #008000; font-weight: bold;">else</span> <span class="kc" style="margin: 0px; padding: 0px; color: #008000; font-weight: bold;">None</span>
<span class="n" style="margin: 0px; padding: 0px;">ax</span> <span class="o" style="margin: 0px; padding: 0px; color: #666666;">=</span> <span class="n" style="margin: 0px; padding: 0px;">fig</span><span class="o" style="margin: 0px; padding: 0px; color: #666666;">.</span><span class="n" style="margin: 0px; padding: 0px;">add_subplot</span><span class="p" style="margin: 0px; padding: 0px;">(</span><span class="mi" style="margin: 0px; padding: 0px; color: #666666;">2</span><span class="p" style="margin: 0px; padding: 0px;">,</span><span class="mi" style="margin: 0px; padding: 0px; color: #666666;">3</span><span class="p" style="margin: 0px; padding: 0px;">,</span><span class="n" style="margin: 0px; padding: 0px;">i</span><span class="o" style="margin: 0px; padding: 0px; color: #666666;">+</span><span class="mi" style="margin: 0px; padding: 0px; color: #666666;">1</span><span class="p" style="margin: 0px; padding: 0px;">)</span>
<span class="n" style="margin: 0px; padding: 0px;">plot_model_pred_2d</span><span class="p" style="margin: 0px; padding: 0px;">(</span><span class="n" style="margin: 0px; padding: 0px;">estimator</span><span class="p" style="margin: 0px; padding: 0px;">,</span> <span class="n" style="margin: 0px; padding: 0px;">X</span><span class="p" style="margin: 0px; padding: 0px;">,</span> <span class="n" style="margin: 0px; padding: 0px;">y</span><span class="p" style="margin: 0px; padding: 0px;">,</span> <span class="n" style="margin: 0px; padding: 0px;">xlabel</span><span class="o" style="margin: 0px; padding: 0px; color: #666666;">=</span><span class="n" style="margin: 0px; padding: 0px;">xlabel</span><span class="p" style="margin: 0px; padding: 0px;">,</span> <span class="n" style="margin: 0px; padding: 0px;">ylabel</span><span class="o" style="margin: 0px; padding: 0px; color: #666666;">=</span><span class="n" style="margin: 0px; padding: 0px;">ylabel</span><span class="p" style="margin: 0px; padding: 0px;">,</span> <span class="n" style="margin: 0px; padding: 0px;">cbar</span><span class="o" style="margin: 0px; padding: 0px; color: #666666;">=</span><span class="kc" style="margin: 0px; padding: 0px; color: #008000; font-weight: bold;">False</span><span class="p" style="margin: 0px; padding: 0px;">)</span>
<span class="n" style="margin: 0px; padding: 0px;">txt</span> <span class="o" style="margin: 0px; padding: 0px; color: #666666;">=</span> <span class="s1" style="margin: 0px; padding: 0px; color: #ba2121;">'Tree </span><span class="si" style="margin: 0px; padding: 0px; color: #bb6688; font-weight: bold;">{}</span><span class="s1" style="margin: 0px; padding: 0px; color: #ba2121;">'</span><span class="o" style="margin: 0px; padding: 0px; color: #666666;">.</span><span class="n" style="margin: 0px; padding: 0px;">format</span><span class="p" style="margin: 0px; padding: 0px;">(</span><span class="n" style="margin: 0px; padding: 0px;">i</span><span class="o" style="margin: 0px; padding: 0px; color: #666666;">+</span><span class="mi" style="margin: 0px; padding: 0px; color: #666666;">1</span><span class="p" style="margin: 0px; padding: 0px;">)</span>
<span class="n" style="margin: 0px; padding: 0px;">plt</span><span class="o" style="margin: 0px; padding: 0px; color: #666666;">.</span><span class="n" style="margin: 0px; padding: 0px;">text</span><span class="p" style="margin: 0px; padding: 0px;">(</span><span class="mf" style="margin: 0px; padding: 0px; color: #666666;">7.0</span><span class="p" style="margin: 0px; padding: 0px;">,</span> <span class="mf" style="margin: 0px; padding: 0px; color: #666666;">3.5</span><span class="p" style="margin: 0px; padding: 0px;">,</span> <span class="n" style="margin: 0px; padding: 0px;">txt</span><span class="p" style="margin: 0px; padding: 0px;">,</span> <span class="n" style="margin: 0px; padding: 0px;">fontdict</span><span class="o" style="margin: 0px; padding: 0px; color: #666666;">=</span><span class="p" style="margin: 0px; padding: 0px;">{</span><span class="s1" style="margin: 0px; padding: 0px; color: #ba2121;">'fontsize'</span><span class="p" style="margin: 0px; padding: 0px;">:</span><span class="mi" style="margin: 0px; padding: 0px; color: #666666;">12</span><span class="p" style="margin: 0px; padding: 0px;">})</span>
<span class="n" style="margin: 0px; padding: 0px;">plt</span><span class="o" style="margin: 0px; padding: 0px; color: #666666;">.</span><span class="n" style="margin: 0px; padding: 0px;">show</span><span class="p" style="margin: 0px; padding: 0px;">()<br /><br /></span></pre>
<p><img height="486" width="727" src="/assets/courseware/v1/91906f34ef31a405f470972311a0beb2/asset-v1:MITx+HST.953x+3T2020+type@asset+block/download.png" alt="" /></p>
</div>
</div>
<div class="vert vert-3" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@be5ad3ca52bb42ffb1d4a92dfb16eed4">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@be5ad3ca52bb42ffb1d4a92dfb16eed4" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<p>Use the following code block to visualize the final decision surface.</p>
<pre style="margin-top: 0px; margin-bottom: 0px; padding: 0px; font-family: monospace; font-size: 14px; color: #333333; border-radius: 4px; line-height: inherit; word-break: break-all; border: none;"><span class="nb" style="margin: 0px; padding: 0px; color: green;">print</span><span class="p" style="margin: 0px; padding: 0px;">(</span><span class="s1" style="margin: 0px; padding: 0px; color: #ba2121;">'</span><span class="se" style="margin: 0px; padding: 0px; color: #bb6622; font-weight: bold;">\n</span><span class="s1" style="margin: 0px; padding: 0px; color: #ba2121;">Plotting final decision surface</span><span class="se" style="margin: 0px; padding: 0px; color: #bb6622; font-weight: bold;">\n</span><span class="s1" style="margin: 0px; padding: 0px; color: #ba2121;">'</span><span class="p" style="margin: 0px; padding: 0px;">)</span>
<span class="n" style="margin: 0px; padding: 0px;">plt</span><span class="o" style="margin: 0px; padding: 0px; color: #666666;">.</span><span class="n" style="margin: 0px; padding: 0px;">figure</span><span class="p" style="margin: 0px; padding: 0px;">(</span><span class="n" style="margin: 0px; padding: 0px;">figsize</span><span class="o" style="margin: 0px; padding: 0px; color: #666666;">=</span><span class="p" style="margin: 0px; padding: 0px;">[</span><span class="mi" style="margin: 0px; padding: 0px; color: #666666;">9</span><span class="p" style="margin: 0px; padding: 0px;">,</span><span class="mi" style="margin: 0px; padding: 0px; color: #666666;">5</span><span class="p" style="margin: 0px; padding: 0px;">])</span>
<span class="n" style="margin: 0px; padding: 0px;">plot_model_pred_2d</span><span class="p" style="margin: 0px; padding: 0px;">(</span><span class="n" style="margin: 0px; padding: 0px;">mdl</span><span class="p" style="margin: 0px; padding: 0px;">,</span> <span class="n" style="margin: 0px; padding: 0px;">X</span><span class="p" style="margin: 0px; padding: 0px;">,</span> <span class="n" style="margin: 0px; padding: 0px;">y</span><span class="p" style="margin: 0px; padding: 0px;">,</span> <span class="n" style="margin: 0px; padding: 0px;">xlabel</span><span class="o" style="margin: 0px; padding: 0px; color: #666666;">=</span><span class="n" style="margin: 0px; padding: 0px;">feat</span><span class="p" style="margin: 0px; padding: 0px;">[</span><span class="mi" style="margin: 0px; padding: 0px; color: #666666;">0</span><span class="p" style="margin: 0px; padding: 0px;">],</span> <span class="n" style="margin: 0px; padding: 0px;">ylabel</span><span class="o" style="margin: 0px; padding: 0px; color: #666666;">=</span><span class="n" style="margin: 0px; padding: 0px;">feat</span><span class="p" style="margin: 0px; padding: 0px;">[</span><span class="mi" style="margin: 0px; padding: 0px; color: #666666;">1</span><span class="p" style="margin: 0px; padding: 0px;">])</span>
<span class="n" style="margin: 0px; padding: 0px;">plt</span><span class="o" style="margin: 0px; padding: 0px; color: #666666;">.</span><span class="n" style="margin: 0px; padding: 0px;">show</span><span class="p" style="margin: 0px; padding: 0px;">()<br /><br /></span></pre>
<p><img height="324" width="532" src="/assets/courseware/v1/94e027699466f0e56129d918da7d5d89/asset-v1:MITx+HST.953x+3T2020+type@asset+block/download-1.png" alt="" /></p>
<p>Again, the visualization doesn't really show us the power of Random Forests, but we'll quantitatively evaluate them soon enough.</p>
<p>Last, and not least, we move on to gradient boosting.</p>
</div>
</div>
</div>
</div>
<div class="xblock xblock-public_view xblock-public_view-vertical" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@vertical+block@b8299ce111154b24ba41282544aeee81" data-init="VerticalStudentView" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="vertical" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<h2 class="hd hd-2 unit-title">Gradient Boosting</h2>
<div class="vert-mod">
<div class="vert vert-0" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@5ae6399ecd584c81a1431ba08cfdc92e">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@5ae6399ecd584c81a1431ba08cfdc92e" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<p>Gradient boosting (GB) is our last topic - and elegantly combines concepts from the previous methods. As a "boosting" method, GB involves iteratively building trees, aiming to improve upon misclassifications of the previous tree. GB also borrows the concept of subsampling the number of columns (as was done in Random Forests), which tends to prevent overfitting.</p>
<p>While it is hard to express in this non-technical tutorial, the biggest innovation in GB is that it provides a unifying mathematical framework for boosting models. GB explicitly casts the problem of building a tree as an optimization problem, defining mathematical functions for how well a tree is performing (which we had before) <em style="margin: 0px; padding: 0px;">and</em> how complex a tree is. In this light, one can actually treat AdaBoost as a "special case" of GB, where the loss function is chosen to be the exponential loss.</p>
<p></p>
</div>
</div>
<div class="vert vert-1" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@fe6e397b2907409a9b6d59a0ec30bfed">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@fe6e397b2907409a9b6d59a0ec30bfed" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<p>The following python code block builds a gradient boosting model using the sklearn package. </p>
<pre style="margin-top: 0px; margin-bottom: 0px; padding: 0px; font-family: monospace; font-size: 14px; color: #333333; border-radius: 4px; line-height: inherit; word-break: break-all; background-color: #f7f7f7; border: none;"><span class="n" style="margin: 0px; padding: 0px;">np</span><span class="o" style="margin: 0px; padding: 0px; color: #666666;">.</span><span class="n" style="margin: 0px; padding: 0px;">random</span><span class="o" style="margin: 0px; padding: 0px; color: #666666;">.</span><span class="n" style="margin: 0px; padding: 0px;">seed</span><span class="p" style="margin: 0px; padding: 0px;">(</span><span class="mi" style="margin: 0px; padding: 0px; color: #666666;">321</span><span class="p" style="margin: 0px; padding: 0px;">)</span>
<span class="n" style="margin: 0px; padding: 0px;">mdl</span> <span class="o" style="margin: 0px; padding: 0px; color: #666666;">=</span> <span class="n" style="margin: 0px; padding: 0px;">ensemble</span><span class="o" style="margin: 0px; padding: 0px; color: #666666;">.</span><span class="n" style="margin: 0px; padding: 0px;">GradientBoostingClassifier</span><span class="p" style="margin: 0px; padding: 0px;">(</span><span class="n" style="margin: 0px; padding: 0px;">n_estimators</span><span class="o" style="margin: 0px; padding: 0px; color: #666666;">=</span><span class="mi" style="margin: 0px; padding: 0px; color: #666666;">10</span><span class="p" style="margin: 0px; padding: 0px;">)</span>
<span class="n" style="margin: 0px; padding: 0px;">mdl</span> <span class="o" style="margin: 0px; padding: 0px; color: #666666;">=</span> <span class="n" style="margin: 0px; padding: 0px;">mdl</span><span class="o" style="margin: 0px; padding: 0px; color: #666666;">.</span><span class="n" style="margin: 0px; padding: 0px;">fit</span><span class="p" style="margin: 0px; padding: 0px;">(</span><span class="n" style="margin: 0px; padding: 0px;">X</span><span class="p" style="margin: 0px; padding: 0px;">,</span> <span class="n" style="margin: 0px; padding: 0px;">y</span><span class="p" style="margin: 0px; padding: 0px;">)</span></pre>
</div>
</div>
<div class="vert vert-2" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@31b4a208d53344458d38d446655e0f52">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@31b4a208d53344458d38d446655e0f52" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<p>Use the following code block to visualize the decision surface.</p>
<p></p>
<pre style="margin-top: 0px; margin-bottom: 0px; padding: 0px; font-family: monospace; font-size: 14px; color: #333333; border-radius: 4px; line-height: inherit; word-break: break-all; background-color: #f7f7f7; border: none;"><span class="nb" style="margin: 0px; padding: 0px; color: green;">print</span><span class="p" style="margin: 0px; padding: 0px;">(</span><span class="s1" style="margin: 0px; padding: 0px; color: #ba2121;">'</span><span class="se" style="margin: 0px; padding: 0px; color: #bb6622; font-weight: bold;">\n</span><span class="s1" style="margin: 0px; padding: 0px; color: #ba2121;">Plotting final decision surface</span><span class="se" style="margin: 0px; padding: 0px; color: #bb6622; font-weight: bold;">\n</span><span class="s1" style="margin: 0px; padding: 0px; color: #ba2121;">'</span><span class="p" style="margin: 0px; padding: 0px;">)</span>
<span class="n" style="margin: 0px; padding: 0px;">plt</span><span class="o" style="margin: 0px; padding: 0px; color: #666666;">.</span><span class="n" style="margin: 0px; padding: 0px;">figure</span><span class="p" style="margin: 0px; padding: 0px;">(</span><span class="n" style="margin: 0px; padding: 0px;">figsize</span><span class="o" style="margin: 0px; padding: 0px; color: #666666;">=</span><span class="p" style="margin: 0px; padding: 0px;">[</span><span class="mi" style="margin: 0px; padding: 0px; color: #666666;">9</span><span class="p" style="margin: 0px; padding: 0px;">,</span><span class="mi" style="margin: 0px; padding: 0px; color: #666666;">5</span><span class="p" style="margin: 0px; padding: 0px;">])</span>
<span class="n" style="margin: 0px; padding: 0px;">plot_model_pred_2d</span><span class="p" style="margin: 0px; padding: 0px;">(</span><span class="n" style="margin: 0px; padding: 0px;">mdl</span><span class="p" style="margin: 0px; padding: 0px;">,</span> <span class="n" style="margin: 0px; padding: 0px;">X</span><span class="p" style="margin: 0px; padding: 0px;">,</span> <span class="n" style="margin: 0px; padding: 0px;">y</span><span class="p" style="margin: 0px; padding: 0px;">,</span> <span class="n" style="margin: 0px; padding: 0px;">xlabel</span><span class="o" style="margin: 0px; padding: 0px; color: #666666;">=</span><span class="n" style="margin: 0px; padding: 0px;">feat</span><span class="p" style="margin: 0px; padding: 0px;">[</span><span class="mi" style="margin: 0px; padding: 0px; color: #666666;">0</span><span class="p" style="margin: 0px; padding: 0px;">],</span> <span class="n" style="margin: 0px; padding: 0px;">ylabel</span><span class="o" style="margin: 0px; padding: 0px; color: #666666;">=</span><span class="n" style="margin: 0px; padding: 0px;">feat</span><span class="p" style="margin: 0px; padding: 0px;">[</span><span class="mi" style="margin: 0px; padding: 0px; color: #666666;">1</span><span class="p" style="margin: 0px; padding: 0px;">])</span>
<span class="n" style="margin: 0px; padding: 0px;">plt</span><span class="o" style="margin: 0px; padding: 0px; color: #666666;">.</span><span class="n" style="margin: 0px; padding: 0px;">show</span><span class="p" style="margin: 0px; padding: 0px;">()</span></pre>
<p><img src="data:image/png;base64,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%0A" /></p>
<p></p>
<p><span style="color: rgb(49, 49, 49); font-family: "Open Sans", "Helvetica Neue", Helvetica, Arial, sans-serif; text-align: justify;">And that's gradient boosting! In the next section, we will apply the techniques we've just learnt on a more complex classification problem.</span></p>
</div>
</div>
</div>
</div>
<div class="xblock xblock-public_view xblock-public_view-vertical" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@vertical+block@e6fd3156c5304820933e70d10e2e0c8b" data-init="VerticalStudentView" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="vertical" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<h2 class="hd hd-2 unit-title">Dataset: Breast Cancer Wisconsin (Diagnostic)</h2>
<div class="vert-mod">
<div class="vert vert-0" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@38cb36c3b8b94cfda771a05f4fdf9bac">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@38cb36c3b8b94cfda771a05f4fdf9bac" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<h4>Introduction</h4>
<p>For this section we are going to extract and explore another dataset that is more difficult to classify. This dataset contains the features describing cell nuclei of breast mass and we are going to identify whether the mass in cancerous or not.</p>
</div>
</div>
<div class="vert vert-1" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@20075841c0344fe1a25c13679d73e490">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@20075841c0344fe1a25c13679d73e490" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<h4>Importing and partition</h4>
<p style="text-align: center;"><img height="500" width="700" src="/assets/courseware/v1/b4d2183ede5f2b4383cfc101d084b1ec/asset-v1:MITx+HST.953x+3T2020+type@asset+block/Screenshot_2020-05-26_at_18.38.08.png" alt="Import and split dataset for training and testing." /></p>
<p>In the first chunk we import the dataset and selected two features with indexes 1 and 29. Also is important to notice that it was used the <span style="font-family: terminal, monaco;">train_test_split</span> tool that would help us to divide or split the entire dataset in a partition for training (70%) and testing (30%). We can select the size by changing the <span style="font-family: terminal, monaco;">test_size</span> parameter.</p>
</div>
</div>
<div class="vert vert-2" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@d620435fbe7e45e3833c5aeb6df600d8">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@d620435fbe7e45e3833c5aeb6df600d8" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<h4>Modeling</h4>
<p style="text-align: center;"><img height="500" width="919" src="/assets/courseware/v1/406460fbce589d6d46d930b395975785/asset-v1:MITx+HST.953x+3T2020+type@asset+block/Screenshot_2020-05-26_at_18.11.11.png" alt="Train the models and plotting results" /></p>
<p>In the second chunk, it's created a <em>for loop</em> to evaluate in each iteration a different estimator or model that were stated in the beginning and plotted the decision areas. The <span style="font-family: terminal, monaco;">fit</span> function helps to train the model feeding the training set with the parameters stated before. Also during the loop the performance is assessed with the <em>area under the receiver curve</em> (AUROC). It's a performance metric that ranges between 0 and 1. The best model should be the one with the value closest to 1.</p>
<p style="text-align: center;"><img height="500" width="719" src="/assets/courseware/v1/579444e84c7c3c2cbac33fe3ff9e7200/asset-v1:MITx+HST.953x+3T2020+type@asset+block/Screenshot_2020-05-26_at_18.36.21.png" alt="Results after running the chunk that build several ensemble models." /></p>
<p>Here we can see that quantitatively, AdaBoost and Gradient Boosting have produced the highest discrimination among all the models (~0.80). The decision surfaces of these models also seem simpler, and less "noisy", which tends to result in improved generalization on the held out test set (this is not a rule, just Occam's razor in action).</p>
<p>Now we will include all features of the breast cancer dataset. As a result, we will no longer be able to easily visualize the models, but we will be able to better evaluate them. We'll also switch to using 5-fold cross-validation to get a good estimate of the generalization performance of the model.</p>
</div>
</div>
<div class="vert vert-3" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@90f4f24a3ef242388f146c55a17f7eea">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@90f4f24a3ef242388f146c55a17f7eea" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<h4>Conclusions</h4>
<p>We can note two things:</p>
<ul>
<li>by using the entire feature set we have dramatically improved performance of the model, indicating that there was more information contained in the other columns.</li>
<li>most of our models are performing relatively equivalently (except for the super simple model, the decision tree).</li>
</ul>
<p>To make appropriate comparisons, we should calculate 95% confidence intervals on these performance estimates. This can be done a number of ways; the easiest is to bootstrap the calculation.</p>
</div>
</div>
<div class="vert vert-4" data-id="block-v1:MITx+HST.953x+3T2020+type@problem+block@f3e24e40092f48328ea51a2ec87d8858">
<div class="xblock xblock-public_view xblock-public_view-problem xmodule_display xmodule_ProblemBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@problem+block@f3e24e40092f48328ea51a2ec87d8858" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="problem" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="True" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "Problem"}
</script>
<div id="problem_f3e24e40092f48328ea51a2ec87d8858" class="problems-wrapper" role="group"
aria-labelledby="f3e24e40092f48328ea51a2ec87d8858-problem-title"
data-problem-id="block-v1:MITx+HST.953x+3T2020+type@problem+block@f3e24e40092f48328ea51a2ec87d8858" data-url="/courses/course-v1:MITx+HST.953x+3T2020/xblock/block-v1:MITx+HST.953x+3T2020+type@problem+block@f3e24e40092f48328ea51a2ec87d8858/handler/xmodule_handler"
data-problem-score="0"
data-problem-total-possible="4"
data-attempts-used="0"
data-content="
<h3 class="hd hd-3 problem-header" id="f3e24e40092f48328ea51a2ec87d8858-problem-title" aria-describedby="block-v1:MITx+HST.953x+3T2020+type@problem+block@f3e24e40092f48328ea51a2ec87d8858-problem-progress" tabindex="-1">
Quiz: Wrapping up
</h3>
<div class="problem-progress" id="block-v1:MITx+HST.953x+3T2020+type@problem+block@f3e24e40092f48328ea51a2ec87d8858-problem-progress"></div>
<div class="problem">
<div>
<p>You can use this template as a guide to the simple editor markdown and OLX markup to use for checkboxes problems. Edit this component to replace this template with your own assessment.</p>
<div class="wrapper-problem-response" tabindex="-1" aria-label="Question 1" role="group"><div class="choicegroup capa_inputtype" id="inputtype_f3e24e40092f48328ea51a2ec87d8858_2_1">
<fieldset aria-describedby="status_f3e24e40092f48328ea51a2ec87d8858_2_1">
<legend id="f3e24e40092f48328ea51a2ec87d8858_2_1-legend" class="response-fieldset-legend field-group-hd">Parameter tuning of a model should be done during ...</legend>
<div class="field">
<input type="checkbox" name="input_f3e24e40092f48328ea51a2ec87d8858_2_1[]" id="input_f3e24e40092f48328ea51a2ec87d8858_2_1_choice_0" class="field-input input-checkbox" value="choice_0"/><label id="f3e24e40092f48328ea51a2ec87d8858_2_1-choice_0-label" for="input_f3e24e40092f48328ea51a2ec87d8858_2_1_choice_0" class="response-label field-label label-inline" aria-describedby="status_f3e24e40092f48328ea51a2ec87d8858_2_1"> data exploration to understand the distribution of each covariate.
</label>
</div>
<div class="field">
<input type="checkbox" name="input_f3e24e40092f48328ea51a2ec87d8858_2_1[]" id="input_f3e24e40092f48328ea51a2ec87d8858_2_1_choice_1" class="field-input input-checkbox" value="choice_1"/><label id="f3e24e40092f48328ea51a2ec87d8858_2_1-choice_1-label" for="input_f3e24e40092f48328ea51a2ec87d8858_2_1_choice_1" class="response-label field-label label-inline" aria-describedby="status_f3e24e40092f48328ea51a2ec87d8858_2_1"> testing a model with data that a model has not seen before.
</label>
</div>
<div class="field">
<input type="checkbox" name="input_f3e24e40092f48328ea51a2ec87d8858_2_1[]" id="input_f3e24e40092f48328ea51a2ec87d8858_2_1_choice_2" class="field-input input-checkbox" value="choice_2"/><label id="f3e24e40092f48328ea51a2ec87d8858_2_1-choice_2-label" for="input_f3e24e40092f48328ea51a2ec87d8858_2_1_choice_2" class="response-label field-label label-inline" aria-describedby="status_f3e24e40092f48328ea51a2ec87d8858_2_1"> data preprocessing when outlier and missing data are removed.
</label>
</div>
<div class="field">
<input type="checkbox" name="input_f3e24e40092f48328ea51a2ec87d8858_2_1[]" id="input_f3e24e40092f48328ea51a2ec87d8858_2_1_choice_3" class="field-input input-checkbox" value="choice_3"/><label id="f3e24e40092f48328ea51a2ec87d8858_2_1-choice_3-label" for="input_f3e24e40092f48328ea51a2ec87d8858_2_1_choice_3" class="response-label field-label label-inline" aria-describedby="status_f3e24e40092f48328ea51a2ec87d8858_2_1"> training a model.
</label>
</div>
<span id="answer_f3e24e40092f48328ea51a2ec87d8858_2_1"/>
</fieldset>
<div class="indicator-container">
<span class="status unanswered" id="status_f3e24e40092f48328ea51a2ec87d8858_2_1" data-tooltip="Not yet answered.">
<span class="sr">unanswered</span><span class="status-icon" aria-hidden="true"/>
</span>
</div>
</div></div>
<div class="wrapper-problem-response" tabindex="-1" aria-label="Question 2" role="group"><div class="choicegroup capa_inputtype" id="inputtype_f3e24e40092f48328ea51a2ec87d8858_3_1">
<fieldset aria-describedby="status_f3e24e40092f48328ea51a2ec87d8858_3_1">
<legend id="f3e24e40092f48328ea51a2ec87d8858_3_1-legend" class="response-fieldset-legend field-group-hd">When using the class "DecisionTreeClassifier", how can we change the parameter that determines how the impurity of split will be measured?</legend>
<div class="field">
<input type="checkbox" name="input_f3e24e40092f48328ea51a2ec87d8858_3_1[]" id="input_f3e24e40092f48328ea51a2ec87d8858_3_1_choice_0" class="field-input input-checkbox" value="choice_0"/><label id="f3e24e40092f48328ea51a2ec87d8858_3_1-choice_0-label" for="input_f3e24e40092f48328ea51a2ec87d8858_3_1_choice_0" class="response-label field-label label-inline" aria-describedby="status_f3e24e40092f48328ea51a2ec87d8858_3_1"> criterion = "entropy"
</label>
</div>
<div class="field">
<input type="checkbox" name="input_f3e24e40092f48328ea51a2ec87d8858_3_1[]" id="input_f3e24e40092f48328ea51a2ec87d8858_3_1_choice_1" class="field-input input-checkbox" value="choice_1"/><label id="f3e24e40092f48328ea51a2ec87d8858_3_1-choice_1-label" for="input_f3e24e40092f48328ea51a2ec87d8858_3_1_choice_1" class="response-label field-label label-inline" aria-describedby="status_f3e24e40092f48328ea51a2ec87d8858_3_1"> criterion = "random"
</label>
</div>
<div class="field">
<input type="checkbox" name="input_f3e24e40092f48328ea51a2ec87d8858_3_1[]" id="input_f3e24e40092f48328ea51a2ec87d8858_3_1_choice_2" class="field-input input-checkbox" value="choice_2"/><label id="f3e24e40092f48328ea51a2ec87d8858_3_1-choice_2-label" for="input_f3e24e40092f48328ea51a2ec87d8858_3_1_choice_2" class="response-label field-label label-inline" aria-describedby="status_f3e24e40092f48328ea51a2ec87d8858_3_1"> criterion = "auto"
</label>
</div>
<div class="field">
<input type="checkbox" name="input_f3e24e40092f48328ea51a2ec87d8858_3_1[]" id="input_f3e24e40092f48328ea51a2ec87d8858_3_1_choice_3" class="field-input input-checkbox" value="choice_3"/><label id="f3e24e40092f48328ea51a2ec87d8858_3_1-choice_3-label" for="input_f3e24e40092f48328ea51a2ec87d8858_3_1_choice_3" class="response-label field-label label-inline" aria-describedby="status_f3e24e40092f48328ea51a2ec87d8858_3_1"> criterion = "gini"
</label>
</div>
<span id="answer_f3e24e40092f48328ea51a2ec87d8858_3_1"/>
</fieldset>
<div class="indicator-container">
<span class="status unanswered" id="status_f3e24e40092f48328ea51a2ec87d8858_3_1" data-tooltip="Not yet answered.">
<span class="sr">unanswered</span><span class="status-icon" aria-hidden="true"/>
</span>
</div>
</div></div>
<div class="wrapper-problem-response" tabindex="-1" aria-label="Question 3" role="group"><div class="choicegroup capa_inputtype" id="inputtype_f3e24e40092f48328ea51a2ec87d8858_4_1">
<fieldset aria-describedby="status_f3e24e40092f48328ea51a2ec87d8858_4_1">
<legend id="f3e24e40092f48328ea51a2ec87d8858_4_1-legend" class="response-fieldset-legend field-group-hd">When using the class "DecisionTreeClassifier", what is the function of the "splitter" parameter?</legend>
<div class="field">
<input type="checkbox" name="input_f3e24e40092f48328ea51a2ec87d8858_4_1[]" id="input_f3e24e40092f48328ea51a2ec87d8858_4_1_choice_0" class="field-input input-checkbox" value="choice_0"/><label id="f3e24e40092f48328ea51a2ec87d8858_4_1-choice_0-label" for="input_f3e24e40092f48328ea51a2ec87d8858_4_1_choice_0" class="response-label field-label label-inline" aria-describedby="status_f3e24e40092f48328ea51a2ec87d8858_4_1"> To select the number of features to consider when looking for the best split.
</label>
</div>
<div class="field">
<input type="checkbox" name="input_f3e24e40092f48328ea51a2ec87d8858_4_1[]" id="input_f3e24e40092f48328ea51a2ec87d8858_4_1_choice_1" class="field-input input-checkbox" value="choice_1"/><label id="f3e24e40092f48328ea51a2ec87d8858_4_1-choice_1-label" for="input_f3e24e40092f48328ea51a2ec87d8858_4_1_choice_1" class="response-label field-label label-inline" aria-describedby="status_f3e24e40092f48328ea51a2ec87d8858_4_1"> The strategy used to choose the split at each node.
</label>
</div>
<div class="field">
<input type="checkbox" name="input_f3e24e40092f48328ea51a2ec87d8858_4_1[]" id="input_f3e24e40092f48328ea51a2ec87d8858_4_1_choice_2" class="field-input input-checkbox" value="choice_2"/><label id="f3e24e40092f48328ea51a2ec87d8858_4_1-choice_2-label" for="input_f3e24e40092f48328ea51a2ec87d8858_4_1_choice_2" class="response-label field-label label-inline" aria-describedby="status_f3e24e40092f48328ea51a2ec87d8858_4_1"> To avoid overfitting by controling the maximum depth of a tree.
</label>
</div>
<div class="field">
<input type="checkbox" name="input_f3e24e40092f48328ea51a2ec87d8858_4_1[]" id="input_f3e24e40092f48328ea51a2ec87d8858_4_1_choice_3" class="field-input input-checkbox" value="choice_3"/><label id="f3e24e40092f48328ea51a2ec87d8858_4_1-choice_3-label" for="input_f3e24e40092f48328ea51a2ec87d8858_4_1_choice_3" class="response-label field-label label-inline" aria-describedby="status_f3e24e40092f48328ea51a2ec87d8858_4_1"> This is how the decision tree searches the features for a split.
</label>
</div>
<span id="answer_f3e24e40092f48328ea51a2ec87d8858_4_1"/>
</fieldset>
<div class="indicator-container">
<span class="status unanswered" id="status_f3e24e40092f48328ea51a2ec87d8858_4_1" data-tooltip="Not yet answered.">
<span class="sr">unanswered</span><span class="status-icon" aria-hidden="true"/>
</span>
</div>
</div></div>
<div class="wrapper-problem-response" tabindex="-1" aria-label="Question 4" role="group"><div class="choicegroup capa_inputtype" id="inputtype_f3e24e40092f48328ea51a2ec87d8858_5_1">
<fieldset aria-describedby="status_f3e24e40092f48328ea51a2ec87d8858_5_1">
<legend id="f3e24e40092f48328ea51a2ec87d8858_5_1-legend" class="response-fieldset-legend field-group-hd">What is the hyper-parameter that controls the number of trees when using "enemble" methods based on decision trees?</legend>
<div class="field">
<input type="checkbox" name="input_f3e24e40092f48328ea51a2ec87d8858_5_1[]" id="input_f3e24e40092f48328ea51a2ec87d8858_5_1_choice_0" class="field-input input-checkbox" value="choice_0"/><label id="f3e24e40092f48328ea51a2ec87d8858_5_1-choice_0-label" for="input_f3e24e40092f48328ea51a2ec87d8858_5_1_choice_0" class="response-label field-label label-inline" aria-describedby="status_f3e24e40092f48328ea51a2ec87d8858_5_1"> criterion.
</label>
</div>
<div class="field">
<input type="checkbox" name="input_f3e24e40092f48328ea51a2ec87d8858_5_1[]" id="input_f3e24e40092f48328ea51a2ec87d8858_5_1_choice_1" class="field-input input-checkbox" value="choice_1"/><label id="f3e24e40092f48328ea51a2ec87d8858_5_1-choice_1-label" for="input_f3e24e40092f48328ea51a2ec87d8858_5_1_choice_1" class="response-label field-label label-inline" aria-describedby="status_f3e24e40092f48328ea51a2ec87d8858_5_1"> n_estimators.
</label>
</div>
<div class="field">
<input type="checkbox" name="input_f3e24e40092f48328ea51a2ec87d8858_5_1[]" id="input_f3e24e40092f48328ea51a2ec87d8858_5_1_choice_2" class="field-input input-checkbox" value="choice_2"/><label id="f3e24e40092f48328ea51a2ec87d8858_5_1-choice_2-label" for="input_f3e24e40092f48328ea51a2ec87d8858_5_1_choice_2" class="response-label field-label label-inline" aria-describedby="status_f3e24e40092f48328ea51a2ec87d8858_5_1"> min_impurity_split.
</label>
</div>
<div class="field">
<input type="checkbox" name="input_f3e24e40092f48328ea51a2ec87d8858_5_1[]" id="input_f3e24e40092f48328ea51a2ec87d8858_5_1_choice_3" class="field-input input-checkbox" value="choice_3"/><label id="f3e24e40092f48328ea51a2ec87d8858_5_1-choice_3-label" for="input_f3e24e40092f48328ea51a2ec87d8858_5_1_choice_3" class="response-label field-label label-inline" aria-describedby="status_f3e24e40092f48328ea51a2ec87d8858_5_1"> max_depth.
</label>
</div>
<span id="answer_f3e24e40092f48328ea51a2ec87d8858_5_1"/>
</fieldset>
<div class="indicator-container">
<span class="status unanswered" id="status_f3e24e40092f48328ea51a2ec87d8858_5_1" data-tooltip="Not yet answered.">
<span class="sr">unanswered</span><span class="status-icon" aria-hidden="true"/>
</span>
</div>
</div></div>
</div>
<div class="action">
<input type="hidden" name="problem_id" value="Quiz: Wrapping up" />
<div class="submit-attempt-container">
<button type="button" class="submit btn-brand" data-submitting="Submitting" data-value="Submit" data-should-enable-submit-button="True" aria-describedby="submission_feedback_f3e24e40092f48328ea51a2ec87d8858" >
<span class="submit-label">Submit</span>
</button>
<div class="submission-feedback" id="submission_feedback_f3e24e40092f48328ea51a2ec87d8858">
<span class="sr">Some problems have options such as save, reset, hints, or show answer. These options follow the Submit button.</span>
</div>
</div>
<div class="problem-action-buttons-wrapper">
</div>
</div>
<div class="notification warning notification-gentle-alert
is-hidden"
tabindex="-1">
<span class="icon fa fa-exclamation-circle" aria-hidden="true"></span>
<span class="notification-message" aria-describedby="f3e24e40092f48328ea51a2ec87d8858-problem-title">
</span>
<div class="notification-btn-wrapper">
<button type="button" class="btn btn-default btn-small notification-btn review-btn sr">Review</button>
</div>
</div>
<div class="notification warning notification-save
is-hidden"
tabindex="-1">
<span class="icon fa fa-save" aria-hidden="true"></span>
<span class="notification-message" aria-describedby="f3e24e40092f48328ea51a2ec87d8858-problem-title">None
</span>
<div class="notification-btn-wrapper">
<button type="button" class="btn btn-default btn-small notification-btn review-btn sr">Review</button>
</div>
</div>
<div class="notification general notification-show-answer
is-hidden"
tabindex="-1">
<span class="icon fa fa-info-circle" aria-hidden="true"></span>
<span class="notification-message" aria-describedby="f3e24e40092f48328ea51a2ec87d8858-problem-title">Answers are displayed within the problem
</span>
<div class="notification-btn-wrapper">
<button type="button" class="btn btn-default btn-small notification-btn review-btn sr">Review</button>
</div>
</div>
</div>
"
data-graded="False">
<p class="loading-spinner">
<i class="fa fa-spinner fa-pulse fa-2x fa-fw"></i>
<span class="sr">Loading…</span>
</p>
</div>
</div>
</div>
</div>
</div>
<div class="xblock xblock-public_view xblock-public_view-vertical" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@vertical+block@de0d5e053435408e9206e10e35f3692d" data-init="VerticalStudentView" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="vertical" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<h2 class="hd hd-2 unit-title">Pipeline Class</h2>
<div class="vert-mod">
<div class="vert vert-0" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@6c87d085dcd24ba782c045c96ddf7909">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@6c87d085dcd24ba782c045c96ddf7909" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<p>In this session, we are going to use a great way, pipeline from scikit-learn, to build models. This allows us to define the steps in our preprocessing with the ultimate model. It's a great feature that simplifies a lot of the tedium in preprocessing. Below we are going to use pipeline from scikit-learn to <span style="font-size: 1em;">impute missing data.</span></p>
</div>
</div>
<div class="vert vert-1" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@7c168d599a6e44a78e19e81db58ee8dd">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@7c168d599a6e44a78e19e81db58ee8dd" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<h4></h4>
<p style="font-size: 16px;">The following python code block defines the features that will be used later.<img height="173" width="800" src="/assets/courseware/v1/dabc84dd35d65e6ad25b7fd51c01252e/asset-v1:MITx+HST.953x+3T2020+type@asset+block/Screen_Shot_2020-06-02_at_8.32.18_PM.png" alt="define_feature" /></p>
</div>
</div>
<div class="vert vert-2" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@724394c6faae49a5841a453c85069a23">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@724394c6faae49a5841a453c85069a23" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<p>In this second code chunk, we use pipeline to automatically apply preprocess the data. We choose the classifier and doing imputation for numeric and categorical data separately.</p>
<p><img height="700" width="800" src="/assets/courseware/v1/a46b25f70274454f7d6e14c5c3442b01/asset-v1:MITx+HST.953x+3T2020+type@asset+block/Screen_Shot_2020-06-02_at_10.41.39_PM.png" alt="1" /></p>
</div>
</div>
<div class="vert vert-3" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@6bcce2f6f7594b83ab3aeb1ce58d4321">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@6bcce2f6f7594b83ab3aeb1ce58d4321" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<p>In the previous code chunk, the pipeline is prepared. We can use the following code block to easily fit it on our data and evaluate the predictions. </p>
<p> <img height="69" width="900" src="/assets/courseware/v1/a8ee0d808f7d0c33a67c4c05b8010cf2/asset-v1:MITx+HST.953x+3T2020+type@asset+block/Screen_Shot_2020-06-03_at_12.08.52_AM.png" alt="2" /></p>
<p>Evaluate the predictions.</p>
<p><img height="83" width="900" src="/assets/courseware/v1/6475c7344c3d68ad7335b22561795bba/asset-v1:MITx+HST.953x+3T2020+type@asset+block/Screen_Shot_2020-06-03_at_12.09.06_AM.png" alt="3" /></p>
<p>The result above is optimistic as we train and evaluate on the same dataset. We can also take advantage of sklearn's cross-validation tools to easily do cross-validation.</p>
<p><img height="111" width="850" src="/assets/courseware/v1/5929b7edd30a2d47f66346f43d927f60/asset-v1:MITx+HST.953x+3T2020+type@asset+block/Screen_Shot_2020-06-03_at_12.09.22_AM.png" alt="3" /></p>
<p>For further information on how to use CV to tweak the parameters of a model, you can check out <a href="https://scikit-learn.org/stable/modules/grid_search.html#grid-search" target="[object Object]">Tuning the hyper-parameters of an estimator </a>in the scikit-learn documentation. You can also check out the <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html" target="[object Object]">Pipeline</a> section in the scikit-learn documentation if you want to learn more about this function.</p>
<p></p>
</div>
</div>
</div>
</div>
<div class="xblock xblock-public_view xblock-public_view-vertical" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@vertical+block@1895acab9678473686abc2886ffe001b" data-init="VerticalStudentView" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="vertical" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<h2 class="hd hd-2 unit-title">Final Exercise</h2>
<div class="vert-mod">
<div class="vert vert-0" data-id="block-v1:MITx+HST.953x+3T2020+type@html+block@4762b3796ec249be96ae75d6ca71ff07">
<div class="xblock xblock-public_view xblock-public_view-html xmodule_display xmodule_HtmlBlock" data-usage-id="block-v1:MITx+HST.953x+3T2020+type@html+block@4762b3796ec249be96ae75d6ca71ff07" data-init="XBlockToXModuleShim" data-graded="False" data-request-token="af4093bc43b811ef8f0d0e08775edbcd" data-block-type="html" data-runtime-version="1" data-course-id="course-v1:MITx+HST.953x+3T2020" data-has-score="False" data-runtime-class="LmsRuntime">
<script type="json/xblock-args" class="xblock-json-init-args">
{"xmodule-type": "HTMLModule"}
</script>
<p>Let's practice all models checked in this workshop with real-world data. It contains patients admitted to intensive care units at the Beth Israel Deaconness Medical Center in Boston, MA. All patients in the cohort stayed for at least 48 hours, and the goal of the prediction task is to predict in-hospital mortality.</p>
<p>The data is originally provided as a time series of observations for a number of variables, but to simplify the analysis, we've done some preprocessing to get a single row for each patient. The following cell will check if the data is available here. If it's not, it will download it to the subfolder data in the same folder as this notebook.</p>
<p style="text-align: center;"><img height="282" width="712" src="/assets/courseware/v1/04eb3e68872011fcd7a2fdfe9cf0dd16/asset-v1:MITx+HST.953x+3T2020+type@asset+block/Screenshot_2020-05-27_at_00.41.47.png" alt="Importing data-set." /></p>
<p>The outcome is the first column 'In-hospital_death'. The rest of the data are features you can use to predict this binary outcome.</p>
<p>Now, using what you've learned above, try to build the five classifiers using decision trees, AdaBoost, Random Forest, Bagging, and Gradient Boosting. Pick your favourite and play with the parameters to see how well you can do! Be sure to use cross-validation to make sure you don't overfit.</p>
</div>
</div>
</div>
</div>
© All Rights Reserved