{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: white;\n            font-size: 250%;\n            text-align: center;\n            display: fill;\n            border-radius: 5px;\n            background-color: #66023C;\n            overflow: hidden;\n            font-weight: 700;\n            border: 5px solid #F28C28;\"\n     >\n    🏆 Machine Learning Grandmaster\n</div>","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19"}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #e8f4ff;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    Welcome to ML Grandmaster, where we'll take a look at the most important Machine Learning algorithms and fundamental concepts from the ground up. <br>\n    We'll look at the theory behind the concepts and also the code to implement these ideas in the real world.\n</div>","metadata":{}},{"cell_type":"markdown","source":"## ML Grandmaster Notebooks\n<br>\n<div\n     style=\"float: left; position: relative; width: 100%;\"\n     >\n    <table\n           style=\"float: left; font-size: 16px\"\n           >\n      <tr>\n        <th><b>📈 Machine Learning Fundamentals</b></th>\n        <th><b>🌳 Decision Trees</b></th>\n      </tr>\n      <tr>\n        <td><a href=\"https://www.kaggle.com/chazzer/ml-grandmaster-linear-regression/\">Linear Regression</a></td>\n        <td><a href=\"https://www.kaggle.com/code/chazzer/ml-grandmaster-decision-tree-classifier\">Decision Tree Classification</a></td>\n      </tr>\n      <tr>\n        <td>Logistic Regression [work in progress]</td>\n        <td><a href=\"https://www.kaggle.com/code/chazzer/ml-grandmaster-decision-tree-regressor\">Decision Tree Regression</a></td>\n      </tr>\n      <tr>\n        <td></td>\n        <td><a href=\"https://www.kaggle.com/code/chazzer/ml-grandmaster-random-forest/\">Random Forest</a></td>\n      </tr>\n      <tr>\n        <td></td>\n        <td><a href=\"https://www.kaggle.com/chazzer/ml-grandmaster-gradient-boosting-xgboost/\">Gradient Boosting (XGBoost)</a></td>\n      </tr>\n    </table>\n</div>","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: white;\n            font-size: 250%;\n            text-align: center;\n            display: fill;\n            border-radius: 5px;\n            background-color: #023c66;\n            overflow: hidden;\n            font-weight: 700;\n            border: 5px solid #7ace07;\"\n     >\n    🌳 Decision Tree Regression\n</div>","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    In this ML Grandmaster notebook, we'll be taking a look at Decision Tree Regression from the ground up. <br>\n</div>","metadata":{}},{"cell_type":"markdown","source":"![regression tree](https://i.kym-cdn.com/editorials/icons/original/000/004/844/wise_mystical_tree_lore.jpg)","metadata":{}},{"cell_type":"markdown","source":"# 👉 Contents\n* [📚 Prerequisites and Notes](#0)\n* [🤨 Intuition](#1)\n* [🚚 Import](#2)\n    - [Packages](#2.1)\n    - [Dataset](#2.2)\n* [♻️ CART Algorithm](#3)\n* [🪓 Mean Squared Error | Splitting Criterion](#4)\n* [🧱 Building the Tree](#5)\n    - [CART Step 1](#5.1)\n    - [CART Step 2](#5.2)\n    - [CART Step 3](#5.3)\n    - [Minimum Samples Split](#5.4)\n* [💻 Code Implementation](#6)\n* [🙇 Conclusion](#7)","metadata":{}},{"cell_type":"markdown","source":"<a id=\"0\"></a>\n# 📚 Prerequisites and Notes","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    There are no prerequisites for this notebook.\n</div>","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    Similar topic: <a href=\"https://www.kaggle.com/code/chazzer/ml-grandmaster-decision-tree-classifier/\">Decision Tree Classification</a>. <br>\n    While the Decision Tree is almost never used in the real world, it is the fundamental building block for more powerful and popular algorithms like Random Forests and XGBoost. <br>\n    Random Forest notebook: <a href=\"https://www.kaggle.com/code/chazzer/ml-grandmaster-random-forest/\">🌳 Random Forest</a>. <br>\n    Gradient Boosting (XGBoost) notebook: <a href=\"https://www.kaggle.com/chazzer/ml-grandmaster-gradient-boosting-xgboost/\">🌳 Gradient Boosting (XGBoost)</a>.\n</div>","metadata":{}},{"cell_type":"markdown","source":"<a id=\"1\"></a>\n# 🤨 Intuition","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    A Decision Tree tries to model one or more output values, given one or more input values. <br>\n    Its structure is that of a Binary Tree, and at each non-leaf node it makes a decision based on an input value via a logical statement that can be either True or False. <br>\n    Based on the decisions in each non-leaf node, it generates an output in the leaf nodes.\n</div>","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    In particular, a Decision Tree Regressor tries to model a continuous variable, a real number indicating some quantity. <br>\n    Let's take a look at a simple Decision Tree Regressor.\n</div>","metadata":{}},{"cell_type":"markdown","source":"![1.jpg](attachment:da777e6f-f632-4215-9121-25fafd214c34.jpg)","metadata":{},"attachments":{"da777e6f-f632-4215-9121-25fafd214c34.jpg":{"image/jpeg":"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/8A38X/ABq1SMwVSzEAAZJPamMrfaLn/nyf/v4v+NH2i5/58n/7+L/jTLPVbW/uJIbdmfyxktt+U/Q1dpKSkroUZKSuir9ouf8Anyf/AL+L/jR9ouf+fJ/+/i/41aopjKv2i5/58n/7+L/jR9ouf+fJ/wDv4v8AjVqigDlLCWWPxTeFYGLsGym4ZHQ9eldD9ouf+fJ/+/i/41hWny+Nbgeob+Qrp6wobP1Zz4b4ZerKv2i5/wCfJ/8Av4v+NH2i5/58n/7+L/jVqitzoKv2i5/58n/7+L/jRVqigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDmfHybvCN0f7rIf/HhTfh++7wlAP7sjr/49Vnxqm/whqHsgP5MKzvhu+7wy6/3bhx+gr0lrl78pfocj0xS9DsKKKK806wooooAKKKKACuU+IjY8KSD1mQfrXV1zfjq0ku/ClyIlLNGVkIHoDz+ldODaWIg33RlXV6Urdi14RXZ4T00f9MQfzJrarF8IypN4U05kIIEIU49Rwa2qjEfxp37v8x0v4cfRBVLU9KstXtTb3sCyp1HYqfUHtV2q19qFrpto9zdzLFEvUsevsPU1nByUlyb+RUrW97Y5aT4c6Em6RpbpIwMnMowB9SK4zVIdKnvU0vw5ZvPIzbTcOxYuf9nsB71pajrOreN9Q/s7TI3isgfmBOMj+85/pXb+HfDFn4ft/wB2PMuWH7ydhyfYegr3Pb1MLHmrzbn0jfb1PP8AZxrO1ONo9/8AIf4Z0QaDoyWhcPKxLysOhY+ntWzRRXiVJyqSc5bs9CMVFKKCiiioKCuI+JoJ0K2IHAuP/ZTXb1WvrC11K1a2vIVmhbBKtW+GqqjWjUfQzrQdSDiupQ8NajZ3+i2y2s6SmCJEkC9VbaODWxVPT9KsdKjeOxto4Fc5YIOpq5UVXGU24beY4KSilLcKw/GFu1z4U1BEBLCPeAPYg/0rcproskbI4DKwIIPcUqU+San2dxzjzRce5yXw4ukm8NGAH54ZmDD2PIrr643RfCd7oPimSeznX+y5FO5GPzey49j3rsq6Ma4SrOdN3UtTLD8ypqMlZrQKKKK5DcKKKKAGv9xvoa8x+G5/4qK+94T/AOh16dJ/q2+hry/4bf8AIx3v/XA/+hCvTwf+7VvRHHX/AI1P5nqVFFFeYdgUUUUAFFFFABRRRQAUUUUAFFFNc7UY+gJoA5nwyd2qX7+v/wAUa6iuX8IjMl4/qV/rXUVhhv4aOfC/wkFFFFbnQFFFFABRRRQB59dvJpmsXXkNsYMyqR2BqvbNK1wGji8+YnI3Ddz64713b6RZSXrXbwhpGGCG5U/hVqKCGAYiiRB/sqBXD9Vk3voef9Tk5avQ56DQr6/Ik1S6cL/zyU/5ArdtLG2sYyltEEB6nufrViiuqFKMdVudcKMYarfuUNZu2stKmlT7+Nqn0J4rF8J2JzJfOP8AYjz39TXTyRpNGY5EV0PVWGQaERIkCRqFVRgADAFKVPmqKT6ClS5qim3oh1ZetaQuqQLtYJNH9xj0Psa1KKuUVJWZpOCmuWRjaHpFzprSNPcB1ZQAikkD8607sstnOUBLCNsAdc4qailGCjHlQo01CPLEwfC1m1vYvNIpV5W6EYOB/k1vUUUQgoRUUFOChFRRQ1fTRqloIfM8tlYMDjNYfiHTxZ6RZxxAmOFiGb3Pf866umyRpLGY5FDI3BVhkGpqUlNPuyKtCM0+7OEe5vNZNtZRJ8sahQo6cDG412WnWMenWSQJyRyzf3j61LBa29sD5EMceeu1cZqappUeR80ndk0aHI+aTuwooorc6AooooAKKKKACiiigAooooAKKKKACkd1jRnY4VRkn0FLSEBlKsMgjBBoAxo/E+nPMyFnRAMh2Xg05vE2mKeJXb6IaH8MaYzlhHIueyucVLF4f0yL/l2DH1ck1zr2/kcq+s+RZsdQt9RiaS3YkKcEEYIq1UVvawWkflwRLGpOcKO9S1vG9ve3OiPNb3twoooplBRRRQAVna3qA0/TncH96/yxj39fwrQZlRSzEBQMkntXHTSPr2qySDP2S2QsPoP6k1jWm4qy3ZhXqOMbR3ZN4QTNxdSHkhQM/U11lc34QH+jXT+rj+VdJSwytSQsIrUkFcnqhk1rXPsUbYigBz9QOf1wK6i4lEFtJMeiKW/Kue8KRFxdXj8s7bc/qf50VvekqfcVf35Rp99yGxS+u/DiWtsMEymORicbV61u2ekWdpbiIQo5x8zOoJY1dAAGAAPpUdzOttbSzOcKiljVRpKKu9bFQoxgry1sjm9CVYfEV9DEMRgMAB2w1SanqF7PqslnaTrbpAhZ3P0yaTwpE0jXd6/V22g/qan1Hw417qD3EdwI0kx5i45rnipukuXuc8YzdFcnV/gL4Zuby6hnkuJmkjDBU3evek8R3kjeVptvzLORux6ela0MNvptlsQbIo1JJP6k1h6FG2o6ncarMOAdsYPb/wDUK0akoqnfVmklJQjSvq/6Zt6fYx6faJBGBwPmb+8fWrVFFdCSSsjqSSVkFFFFMYUUUUAcwvyeOD/tD/2Wunrl7n5PG0J/vBf/AEE11FYUfterOfD/AG15sKKKK3OgKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAMfxUnmeFtSX/pgx/Lmuf+GMm7RrxP7txn81FdVrUfm6Hfp627/+gmuM+FsmbbUY/wDbjb9DXpUdcDUXZo5J6YmPoz0GiiivNOsKKKKACiiigApGUMpVgCpGCD3paKAI4YIraFYoI0jjX7qIMAfhUlFFDd9WBFctKttK0Ch5ghKKTwWxwK80tvDHiPxLeLJrkssEEZ/5aEZ+iqP516hRXVh8VKgnyJXfXqvQxq0VUa5np2KWmaVZ6PaLbWUIjQdT3Y+pPc1doormlJyfNJ3ZqkkrIKKKKQwooooAKKKKACiiigAooooAKKKKACiiigAooooAa/MbfQ15d8OTjxNdj1gb/wBCFepnkYrynwEfL8Zzp6xyj8mFengtcPXXkv1OPEfxafqerUUUV5h2BRRRQAUUUUAFFFFABRRRQAVDdtss529I2P6VNVPVn8vSbpv+mRqZO0WyZu0WzG8Hj/RrpvV1H6V0tc/4SXGmyt6y/wBBXQVnh1+6RlhlalEKKKK2NwooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACikZgqlmIAHUmoY721mfZHcRO3orgmldCbSJ6KKKYwooooAKKKKACiiigAooooAKKKKACiiigAooooAZM7Rwu6IXZVJCjufSuZl8RalK3kQWBSbuCpYj8K6mjAznvWc4Sls7GVSEpfDKxzdrb+I5ZPMkuViUnJD4P6Cukoopwhydbjp0+Rbt+oUUUVZoFFFFAGP4hivbizSCzjLCRsSEHoP8Kki0+PTdDmhTl/KYu3944rUqvff8g+5/65N/Ks3BXc+plKmrufWxi+EP+PCf/roP5CuirnfCH/HjP/10H8hXRVOH/hInDfwomT4jm8nRZgOshCfmad4eh8nRYPV8ufxNUvFzEafCvYy/0Na+nKF0y1A6eUv8qS1rPyRK1xD8kWq5vxPfFvL06D5pJCC4H6CtbVNSi021MrkFzwif3jWN4esJLq5fVbvlmJMee59f8KVaTk/Zx3f5BXk5P2Ud3v6G5ptmLCwitx1UfMfU96t0UhIAJJwB1NbpJKyOhJRVkYXia7ZbeOxh5luGwQPT/wCua1dPtFsbGK3X+Ecn1PesHTAdW8Qz37DMMPEf9P6munrGl70nU+4wo+/J1PkvQKKKOtbnQFFFFABRRRQBy+pfL4vsz67f611FcvrXy+KLBv8Ac/8AQjXUVhR+Kfqc9D4p+oUUUVudAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAQ3i77KdP70bD9DXnfwvbF5qMf+wh/IkV6Sw3IR6jFeZfDg+X4j1CL/AKZH9Hr0sLrhay9PzOStpWpv1PTqKKK806wooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiimSyxwRPLK6pGgyzMcACjcB9FcHeeOb7UbtrTw5p7XBHWZ1J/HHYe5p2k+JNetfEdvpWvQxj7SPkKqAQTnB447Yrt/s+sotuydr2vrb0Of61Tbsvv6HdUUUVxHQFeT+Dv3fxBkX3nH6mvWK8o8M/J8SZB/wBNpx/OvUwH8Gsv7v8AmceK+On6nq9FFFeWdgUUUUAFFFFABRRRQAUUUUAFZfiJ9mh3H+1hf1FalYfit9ukBf70qj+dZ1namzKu7U5eg/wum3RlP952P61s1naCmzRLUeq5/M1o0UlaC9B0VanFeQVma7qEmnaf5sWPMZgqkjp71p1i+KYy+jMw/gdW/p/Wiq2oNoVZtU20YsVre3l6LabU3W4ZA+3LEYxnGR3qzLb63oy+elwbiFeWGSRj3B/pUWkj7T4lWUNjbGH+vygY/WuukAMbBvu4Oa5qVNTi3ezOWjSU4uV2nfe5U0zUY9SshOvykcOpP3TVqOaOUExyI4H91s1x+lbv7D1cITtwMfr/AEqPTdGmu7NbqyuwkoJDJkjB+oqo15NLS5UcTNqOl20dvRXN6Xql9b6kNN1HLM3CseoP17iukrohNTV0dNOoqiugorLutds7e4e2D7p1GADwu7sCaqweIZEvFttRtTbM2NrZ4/H2pOrBO1xOvTTtc3qxrvxJa2d89tJFIdhALjH8q2a5Ob7JL4uMbBJYpRscHs2P/rVNaUopcvVk15yilyvdm/b6tYXIHlXUZJ7E4P5Grtc3qXh3ToLWW4Ejw7RkfNkZ7DmrHhaaWbSyJCSEcqhPpShUlzck0KFWfPyTWvkblRzzJbQPNIcIiliakrnfFl3stYrVTzKdzfQf/XrSpPki5GlWfJByJNH8QSajfvbyRKgILR468djW9XB+Hjs1y3/2g38jXeVlhpucLyMsJUlOF5HO+LWYWlugJCNId35Vi2WmpqTMtrHNEygkSOcqSO3A4roPFUYbSN3dJFP9Kz9C1my0/TfKndt5dmwqk8VjVjF1rTehz1Yxde03pYt6Bqdw1xJp16SZo87S3XjqDXQ1yGlXCXviaS8ZkiU52qxwTxgD611N1OtraSzt0jUtit6Erwd3sdGGnem7vRfkTUVheGJJriC6uZnZjJL0J4H0/Ot2tIS54qRtTnzxUgornNdv7wanb2Vi7K+AxAONx7A+3FdDGXMSGQAOQNwHY0RmpSaXQUailJxXQdRWR4iv5LHTgYXKSyMFVh27mtO3ZntomY5YoCT74pqacnHsNTTk49iSiiuP1G5vrzWmsZLhraPfsQAHB9OnXNTUqci2Jq1VTSdr3Ov3LnGRn60tcdaaNbXV/Narezu0Qy0igbc+nWtyw0ZrC5Ei307x4IMbdDUwqTl9nT1Ip1Zz+zp6mrRSEhVJJwByags72C/g86BiyZK5IxyK1ur2N7q9ixRRWZd6sIdTgsYYvNkc/Pg/cFKUlHVilNRV2adFFFUUFFFFABRRRQAUUUUAFR3EZltpYx1ZCv5ipKKGDVznfCSslrdIwIKyAfpXRUgAHQAZ54painDkiomdKHJBRMTxRbNPpW9AT5ThiB6dKzYPFIt9OihW3LTIgXJPy8V1tQLZWqyGRbaIOTnOwZrOdKTlzQdjOdGbnzwdrnM2WlXms3YvNSLLF2U8Fh6AdhXWIiogRAFVRgAdqWirp01BeZdKkqa8+4Vj+I7xrbTvKjz5twdi49O9bFNaNH270VtpyMjODVTi5RaRVSLlFpOxT0ixGn6dHCR85+Z/qazNT1i7kv8A+z9MUGUfefrz3FdDXIHSNYtr24+ygYlJ/ehh0Jz16isavNGKjDbyMK3NCKjBO3kV7vUNXjkayluVkkkG0pHgkZ7ZA611mmWzWenQQOSXVfm5zzVLSNCj08+dKwluT/F2X6f41sUUacl70h4elKPvT3Ciiiug6QooooA5bX+PENgf93/0KuprlvEP/IfsP+A/+hV1NYUvjn6nNR/iT9Qooorc6QooooAKKKKACiiigAooooAKKKKACkZgqlmIAHUmlpGUMpVgCD1BoAj+1W//AD3i/wC+xR9qt/8AnvF/32KPstv/AM8Iv++BR9lt/wDnhF/3wKAD7Vb/APPeL/vsUfarf/nvF/32KPstv/zwi/74FH2W3/54Rf8AfAoAPtVv/wA94v8AvsUfarf/AJ7xf99ij7Lb/wDPCL/vgUfZbf8A54Rf98CgA+1W/wDz3i/77FH2q3/57xf99ij7Lb/88Iv++BR9lt/+eEX/AHwKAD7Vb/8APeL/AL7FH2q3/wCe8X/fYo+y2/8Azwi/74FH2W3/AOeEX/fAoAPtVv8A894v++xR9qt/+e8X/fYo+y2//PCL/vgUfZbf/nhF/wB8CgA+1W//AD3i/wC+xR9qt/8AnvF/32KPstv/AM8Iv++BR9lt/wDnhF/3wKAD7Vb/APPeL/vsV5l4KkSDxvfBnVVKygEnA+/Xpv2W3/54Rf8AfArzLwxGg+I13EyKV3TjaRx1r0sD/BrLyOTE/wASm/M9N+1W/wDz3i/77FH2q3/57xf99ij7Lb/88Iv++BR9lt/+eEX/AHwK806w+1W//PeL/vsUfarf/nvF/wB9ij7Lb/8APCL/AL4FZWlappur3d7BbWw/0R9jOYxtY89PyqlCUk5JaLcTkk0n1NX7Vb/894v++xR9qt/+e8X/AH2KPstv/wA8Iv8AvgVmvqOmR67Fo7Qj7VJGZB+7G3H19eKIwlK/KgcktzS+1W//AD3i/wC+xR9qt/8AnvF/32KPstv/AM8Iv++BR9lt/wDnhF/3wKkYfarf/nvF/wB9ij7Vb/8APeL/AL7FH2W3/wCeEX/fApPstv8A88Iv++BQAv2q3/57xf8AfYo+1W//AD3i/wC+xR9lt/8AnhF/3wKQ21sOsMQ/4AKAF+1W/wDz3i/77FH2q3/57xf99ij7Lb/88Iv++BXFa/4qj0TxOLRrSKS0SHLosY3FzyOe3atqFCdeXLBa7mdSpGmryO1+1W//AD3i/wC+xWfq/iLTdFthPczBgx2qkeGZvwqOy1PTLvQU1d4ooLcoXbeo+XHBFeVeItTk1u8lv1i8q0RvKhULj3/PufwrrwWBdaq4z0S39exjiMSqcLx3ex7Jb6jZ3VtHcRXEZjkUMpLAcGpftVv/AM94v++xWZ4esIofD2nxyQxlxApOVGeRmtP7Lb/88Iv++BXDUiozaWx0QbcU2H2q3/57x/8AfYo+1W//AD3i/wC+xXG+IWUeOdBtIsKudzoowCCe479K7L7Lb/8APCL/AL4FXUounGMm/iV/xJhU5m12D7Vb/wDPeL/vsVgxeOdBlvDbfayhDbRI6EIT9a1r9La10+5uDBFiOJn+6OwrzDwdoVh4ih1C3utyTrteORDyoOQeO4zXThMPSnTnUq3srbeZlXqzjOMIbs9WW7tmUMtxEVPIIcYNL9qt/wDnvF/32K8yew8Q+CpS8caXun5yfk3Lj3HVT+ldX4f8TaPr2IhDHBd4/wBS6jn/AHT3qa2ClCPtKb5o91+q6Dp4hSfLJWZ0X2q3/wCe8X/fYrmvHCT33hx4bCVXbepeNGBLr6dfXBrpfstv/wA8Iv8AvgUfZbf/AJ4Rf98CuajUdKoproazhzxcX1Of8HT2KaBGsVt9iKsVdZWG52HVvfNYOszx3XxO0xY5E2wKu5gRju1d1PHaQQSTSxRBI1LMSg4ArgfBNv8A2z4k1HWpol8sEhFK8At0/JR+td+Hld1cQ9rP72c1WNlCku/5HoX2q3/57xf99ij7Vb/894v++xR9lt/+eEX/AHwKPstv/wA8Iv8AvgV5h2B9qt/+e8X/AH2K8r0F0X4lSMXUJ583zE8dDXqn2W3/AOeEX/fAry3Qo0PxMlQopUTzfKRx0Nenl/8ADrf4TjxXx0/U9S+1W/8Az3i/77FH2q3/AOe8X/fYo+y2/wDzwi/74FH2W3/54Rf98CvMOwPtVv8A894v++xR9qt/+e8X/fYo+y2//PCL/vgUfZbf/nhF/wB8CgA+1W//AD3i/wC+xR9qt/8AnvF/32KPstv/AM8Iv++BR9lt/wDnhF/3wKAD7Vb/APPeL/vsUfarf/nvF/32KPstv/zwi/74FH2W3/54Rf8AfAoAPtVv/wA94v8AvsUfarf/AJ7xf99ij7Lb/wDPCL/vgUfZbf8A54Rf98CgA+1W/wDz3i/77Fc94suI3tLdI5Fb5yTtYHtXQ/Zbf/nhF/3wK5nxZHGhtUjRVzuJ2qB6VhiP4TOfFO1Jm5p00EWm2yGaMFY1yN49Ktfarf8A57xf99imRWkCwoDBHkKB9wU/7Lb/APPCL/vgVtFWVjeKskg+1W//AD3i/wC+xVXUWt7rTriHzosshx8469qtfZbf/nhF/wB8CqmpKlrp080NtG0iLlRsBpStyu4p25Xc5jw1KqauGkcKPKIyTj0rZ8QaxHBam2t5FeaUYJU52j/GuWsnuPtgNuged8gDaD19uldRpHh4W7/ab3bJOeQnUL/ia4aEpShyR+887DynKn7OH3lU26WHhWeMSx+fIAzhWGeo4/KrXhieBNJKs8aN5hzkgE1f1O0hbS7oLDGG8skEKPSsTw1Jbppt5JcIhWJtxLKDxitrKFWK6WN+VU60V0sGpSxyeK7Qo67V2ZYHjqT1robi+ght5ZRNGSikgbhzXO+H7b+0NTuL+WNfLBO1dvGT/gK1Nfjhh0adkhjDHABCjuadN2hKp3uOlK0J1O92YOmWEN/p95LNIgnZv3ZZgDnr+uak1J2l8OWn2jH2mN9vLAtjn/61a/hy1iOjRM8SMWZjkqD3rP8AFLQhre0hjQSE7mCqAfQVk4KNHm7oxdOMaHN1aNZb9I9AEvmp5q2+cbhnOK5RNtvaWd4rbpvPZmGeeMY/rXVX1nFDoMyCJN6QYztGcgVyxKHSbFQF3mds8c44/wAaK+6T7fqgxO6T6L9URX2o3WpzbpmJUH5Y16CtC21DWIoEitooo4lGAML/AFNdJ9o04X62QSMzkE4VAQPqamnWytojLOkKIO7KKuNDVy5y44fVy5zE07xBci9W11FEUvwHGBg+/asjXbsXeqTurApHiNMH8/60++uo9R1Lz7aFUgt13E7cZAOcn6niqNpbT3t0kEIUyMS/PT8a56lRyXInfU5qtSUl7NO+pfslS18RWq712qFy2eM7ea7P7Vb/APPeL/vsVxkdvNb+JbeK7KSSF13YHByK7P7Lb/8APCL/AL4FdWG2kvM7MJopLzMjxLPE+jOqSoxLrwGB71maRo2n3NnHcXU5DNnKbwB1/OrvilYYdNjVI0VnkHIUA8A1c0uO0h0yzjmEIldBgMBkk80nGMqz5uiFKMZ4h83RFK+0HTZYy1pPHDKOg8wFT+tZb6vM+i3FjcNmVGVQxOSRnke/SuxNtbgEmGIAdygrgdWeKXVbhrfHllsLtHB+lRiIqmrx0voZ4qKpLmhpfQ6zw+8EGjQK0sYZssQWHc1p/arf/nvF/wB9iobWygjtIUMMZKoAcqPSnTxW0FvJK0MWEUt9wdq6oLlil2O2C5YJdkc7ayx3Pi+adnXy484JYY4GBXTfarf/AJ7xf99iub8K2yTfariWNWywUZXPua6T7Lb/APPCL/vgVnh/g5u5lhfg5u7bOa8UTpPcWcKSKy5JODkckCukW4t1UKJ4sAY++K5rxBCkes2G2NVQ4BAXAPzV032W3/54Rf8AfAop/wASfyCl/Fm/QPtVv/z3i/77FMee1PzGWEsvIJYcU/7Lb/8APCL/AL4FMktrcROfIi4U/wAArdm7Oe8Kyxo168kiKWYfeYDPWuk+1W//AD3i/wC+xXOeE4o5YrsvGjYZcblB9a6P7Lb/APPCL/vgVhh/4SMML/CRHNPBLBJGLiMFlK53juKwvCt2kUFxbSyKpR9wyQM9j/Kuh+y2/wDzwi/74FcxcRx6T4nV3Rfs0/YgYGev5Gir7soz/rUK3uzjP5fedJLe20UTyGaMhQTw4rC8Oskkt1qNxIgllfC7mAIHf+n5VP4laC20sqkUYeZgoIUZx1NXtM0+GDTbeN4ULhAWJUZyeab96ql2B+/WS7fmW/tVv/z3i/77FH2q3/57xf8AfYo+y2//ADwi/wC+BR9lt/8AnhF/3wK2OgPtVv8A894v++xR9qt/+e8X/fYo+y2//PCL/vgUfZbf/nhF/wB8CgA+1W//AD3i/wC+xR9qt/8AnvF/32KPstv/AM8Iv++BR9lt/wDnhF/3wKAD7Vb/APPeL/vsUfarf/nvF/32KPstv/zwi/74FH2W3/54Rf8AfAoAPtVv/wA94v8AvsUfarf/AJ7xf99ij7Lb/wDPCL/vgUfZbf8A54Rf98CgA+1W/wDz3i/77FH2q3/57xf99ij7Lb/88Iv++BR9lt/+eEX/AHwKAD7Vb/8APeL/AL7FH2q3/wCe8X/fYo+y2/8Azwi/74FH2W3/AOeEX/fAoAPtVv8A894v++xR9qt/+e8X/fYo+y2//PCL/vgUfZbf/nhF/wB8CgA+1W//AD3i/wC+xR9qt/8AnvF/32KPstv/AM8Iv++BR9lt/wDnhF/3wKAD7Vb/APPeL/vsUfarf/nvF/32KPstv/zwi/74FH2W3/54Rf8AfAoAPtVv/wA94v8AvsUfarf/AJ7xf99ij7Lb/wDPCL/vgUfZbf8A54Rf98CgA+1W/wDz3i/77FH2q3/57xf99ij7Lb/88Iv++BR9lt/+eEX/AHwKAD7Vb/8APeL/AL7FH2q3/wCe8X/fYo+y2/8Azwi/74FH2W3/AOeEX/fAoA5jXpY312xZXVlG3JBBA+aun+1W/wDz3i/77Fcxr0Ua69YokaqDtyAAAfmrp/stv/zwi/74FYUvjmc1H+JP1D7Vb/8APeL/AL7FH2q3/wCe8X/fYo+y2/8Azwi/74FH2W3/AOeEX/fArc6Q+1W//PeL/vsUUfZbf/nhF/3wKKAJaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK8w0D5fildD/AKaT16fXmOj/AC/Fe5H/AE1m/lXpYD4K3+FnJifih6np1FFFeadZFdMVtJmHUIx/SuJ+GIzpl+3czjP/AHzXaXn/AB43H/XNv5GuL+GH/IKvv+u4/wDQRXfR/wB0q+sTmqfx4fM7uvPb5iPi3a8n7ij/AMcNegSSJEheR1RR1ZjgCvOZ7iG6+K9rLbypLGQo3I2RkIe9PAJ3qP8AusWKekV5o9IooorzzqOM8YazfHUrTQNLl8qe6x5koOCoJ4Ge3Qmuh0PSE0TTVtFnlnO4uzyHJJPX6CuOlPmfF6MN/CoA/wC/deh134r93Sp047NJvzbOWj785TfR2CuF+I988CaXbxMQ5m83AP8Adxj9TXdV5jqMn/CTfEeC2jO63tWCkjphOWP58UZdBe29pLaKbYYuXucq3eh6YhJRSepHNeP+N1d/GN6UAJRFY59Aor2KvHfGz7PF2o84zGo/Ra3yf+O/T9UZ4/8Ahr1JtH0TXvEOn2lkS1vpMXzB2GFbJOSP7x6+wqz4vsLe11DRtBsk2xKASO7M7AZPvxXe+GYTb+GdOjI5ECn8xn+tcXdf8TD4sxJ1WF1/8dXP866aWKlUrze0YKT079/UynRUaUe8rI9IRBHGqKMKoAFOoorwD0zz2Z/tvxbiUHK26gfTCE/zNehV5x4S/wBP+IGq3p5CeYR+LYH6CvR69DMFyyhDtFHLhdVKXdsyPFLbPC+pMP8Angwrz34dytb+JVjJ4uLdv0Of6Gu/8W/8ipqX/XE15z4LOPFel4/55P8A+z11YFXwVVev5GOIdsRD+up7AQCMHpXnvjvw/aWFsutWA+zXCyqGEfAYnoR6GvQJJEijaSRgqKMszHAArzHWtRuPHGuQ6VpoIsomyZCOD2Ln29K5stU1V507RW/axti3Hk5Xq3segaHePqGh2V3LjzJYlZsevetCobW2js7SG2iGI4kCKPYCpq4JtOTcdjoimopM5H4h6k1l4e+zoSHu38skf3Ryf6Cr/hKxi0nw9a27OgnlHmONwyWbnH5YqXxH4cg8R2kUMszwtExZHQZxkY5FZGj/AA/s9Ou47q4u5bmWJg6D7qgjv713xqUXhFTcrO93pv2OZxqKvzpXXqdjRRRXnHWFeWaCP+LoT/8AXef+Rr1OvLvDP734mXTjkCSdv6V6eA/h1v8ACcmJ+On6nqNFFFeYdYUUUUAFFFFABRRRQAUUUUAFcr4o+fVLKP1H82rqq5fWh5niixT02f8AoRrDEfBb0ObFa07eaOoHAooorc6QooooA5LXW+w+IbSdAFAVTgD35rrRyM1ka1ozao0DxyKjRnB3dxWsBhQPQVjTi4zl2MKUJRnK+zGXC77aVfVCP0rzhJ3S1e2DbUkcF/fHSvS6zJPD+mSdbYA5z8rEVFejKpZxIxNCVSzizGtNbW2t47HS7R5nHVm/iPc4FXZ7XVLzRrpbwx+Y2GjjQcjHJFbFtZ29mmy3hSMew61PVRpO1pMcaEuW0392xxdh4ifT9OFoLfdKpO0k4HJ7irui6VcXN5/ad/u3Z3IrdSfX6eldGYIi+8xIW/vFRmpKUaDuuZ3S2FDDu655XS2GyIskbIwyrAg/SuY/4RaSGWSRJ9yxgtCmOS3bNdTRWk6UZ/EbVKUKlubocHZR6taTP9ntZRM/Bdo8kfieK0IfDuoX8gl1G4Kj0Lbm/wABXWUVlHDRWjd0YRwkVpJtow9T0oQaFJbWEJJLKWA5Zuf1pPDmkvZRNcXC7Z5OAp6qtbtFaexjzqXY19hHnU+xyniOKW01aDUEQlPl59welb+m341G188QvF8xGGq2yqwwwBHoRS9KI03GbknowhScZuSej6HN+LIJ5YYJI4y0Ue4uR26VgWd2kMomNubm4ByhcnC/gOtehkZGD0piQxRnKRop9lArOph3KfOmZVMK5T50zlfs+ua1xMxggPY/KPy6mqmsabHpU9oFDPHjc7kdTnn9K7ikZVddrKGHoRmiWGTW+vcJYRNb692MglWeCOVM7XUMMjHFZviOYw6LMB1chPzNawGBgdKq6jYx6jZtbyMVBIIYdiK2mm4NLc3qRk4NLcxtI1Cz0vRIjNKPMcl9i8sefSmLqmp61KY7BPs8IPMp5I/H+gqW18J20ThriVpsfw42g/Wt+ONIYxHGiog4AUYArGEKjSUtEjCnTquKjJ2S7bmfqGlDUBal5irwsCWC/e9f5VpUUVuopNtHSopNtdQqOfi3kP8Asn+VSUjqHRlPRhiqY3sc34P/AOPa6P8Atj+VdLWHo+gyabdvNJcbgMhFXIBB7mtysaCcYJSRjh4yjTUZKwVR1TTItUtvLc7XU5RwOhq9VTU7wWGnyzn7wGFHqx6VpPl5XzbGlRR5XzbHKW9rcXOtQ2E9x56W7ZJzwAOo/kK7asDwvZsltJey8yTngn0/+ua36yw8bRv3McLC0Obv/SCiiitzpCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOX1v5vE1gv+7/6FXUVy+ofvPGNov93b/U11FYUfik/M56HxTfmFFFFbnQFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXldveQaf8TL67uX2QRSTM7YzgY/+vXqleLeJ7SY+M762VW33EwCj1DYxXq5VFTlOEnvE4sa3FRkujPZYJ47m3jnibdHIoZW9QelSVFawi2tIYF6RoEH4DFS15btfQ7Fe2pBfHFhcH0ib+RrxrToNYudB8nTreaSE3W6Tyeu4KMA47V7NeRmWynjUZZ42UfiK4z4ZNt0u+gPDxz/MPTjH9K9PBVvY0Kk0r2a/U48RT9pVjG9tyrb+Dtb12RbjxBqEiJ2hVsn8ug/WqcWmW2kfE2ysrRCsKBSMnJJKHJNen159rC/Z/ippsrcLKqYP4MK0w2LqVnOMtFyuyWiJrUIU1Fre61PQaKKK8c7zzbXJP7J+J1pey8Qy7Du9iNhr0mua8Y+Gm1+wRrfaLyAkx7uAwPVa5KHxL4u06BbB7F3kUbVeS3Zmx+HBr1nS+uUoODXNFWaf4M4VP2E5KS0ep1fjPxIuiaaYIHH26cEIB/AO7H+lUfh9oD2Nk+p3KkXF0PkDdVTrn8etZugeD7/VNQGreIS5BO4RSfec9tw7D2r0YAAAAYA7VnXqQoUvq9J3b+J/oXSjKrP2s1ZLZC15n4y8N6hf+LI5La3eSK6VAZFHCEcHJ7cc16ZRXNhcTLDz549rG1aiqseVkcMSwQRxL91FCj6AYrzjRHU/E7UZ5WCrGZmLE8ADA/lXpdea6x4L1e68T3UlphbS7bc8xcDAOCQR16iujASh+8jUla63McSpe64q9meiWt3b31slxazJLC/3XQ5BqRyQjEdQKr6fYQaZYQ2duu2KJdo9/Un3NWq4JcvM+XY6le2u55t8NpYhqepCSRRPJjYpPLYJJxXpNY+n+GNK0zUpb+2tys8hJyWJC564HatiunG1oVqzqQ62McPTlThyyMfxUhfwtqQAJPkMeK8u8JX1tZ+IbW6u5RFBbxPuY/jj69a9lnhS4gkhkGUkUqw9QRiuHt/hjZpM5uL6aSPd8iooU49Cea68DiaMKM6VV2v+uhjiaNSVSM4LYytV1zUvGt8NL0mJ47PPzZ43D+857D2ruvD/AIftfD9gIIRulbmWUjlz/h7Vb07S7LSbYW9lAsUfU46sfUnvVyubEYpTj7KkuWC/H1NaVFxfPN3kFFFFcR0BRRWDrvizT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\n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    The Decision Tree Regressor above was constructed based on rules that we decided. <br>\n    Let's see how to build a Decision Tree Regressor starting from a dataset. <br>\n    We'll be using a very simplified version of the house prices dataset. We'll try to predict a house's selling price based on some input features.\n</div>","metadata":{}},{"cell_type":"markdown","source":"<a id=\"2\"></a>\n# 🚚 Import","metadata":{}},{"cell_type":"markdown","source":"<a id=\"2.1\"></a>\n## Packages","metadata":{}},{"cell_type":"code","source":"import numpy as np  # linear algebra\nimport pandas as pd  # data processing\n\nfrom sklearn.tree import DecisionTreeRegressor  # model\n\nfrom matplotlib import pyplot as plt  # visualization\nfrom sklearn.tree import plot_tree  # visualize tree","metadata":{"execution":{"iopub.status.busy":"2022-10-03T08:34:21.136084Z","iopub.execute_input":"2022-10-03T08:34:21.136556Z","iopub.status.idle":"2022-10-03T08:34:22.439686Z","shell.execute_reply.started":"2022-10-03T08:34:21.136465Z","shell.execute_reply":"2022-10-03T08:34:22.438060Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"2.2\"></a>\n## Dataset","metadata":{}},{"cell_type":"code","source":"df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')","metadata":{"execution":{"iopub.status.busy":"2022-10-02T10:29:56.269534Z","iopub.execute_input":"2022-10-02T10:29:56.270884Z","iopub.status.idle":"2022-10-02T10:29:56.326928Z","shell.execute_reply.started":"2022-10-02T10:29:56.270823Z","shell.execute_reply":"2022-10-02T10:29:56.325940Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def preprocess(df):\n    cols_to_drop = [col for col in df.columns if col not in ['LotArea', 'SalePrice']]\n    df['Pool'] = (df['PoolArea'] > 0).astype('int64')\n    df['NewConstruction'] = (df['SaleType'] == 'New').astype('int64')\n    df['SalePrice'] = df['SalePrice'] // 1000\n    df = df.drop(columns=cols_to_drop)\n    df = df[['NewConstruction', 'Pool', 'LotArea', 'SalePrice']]\n    \n    return df","metadata":{"execution":{"iopub.status.busy":"2022-10-02T10:29:56.328716Z","iopub.execute_input":"2022-10-02T10:29:56.329241Z","iopub.status.idle":"2022-10-02T10:29:56.338852Z","shell.execute_reply.started":"2022-10-02T10:29:56.329154Z","shell.execute_reply":"2022-10-02T10:29:56.337184Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df = df.loc[df.index.isin([197, 810, 11, 18, 1449])]\ndf = preprocess(df)\ndf","metadata":{"execution":{"iopub.status.busy":"2022-10-02T10:29:56.341298Z","iopub.execute_input":"2022-10-02T10:29:56.341730Z","iopub.status.idle":"2022-10-02T10:29:56.393197Z","shell.execute_reply.started":"2022-10-02T10:29:56.341700Z","shell.execute_reply":"2022-10-02T10:29:56.391919Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"3\"></a>\n# ♻️ CART Algorithm","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    How do we determine the shape of our Decision Tree Regressor and which features to process in each node? <br>\n    We do this by using the Classification And Regression Tree (CART) Algorithm. <br>\n    Let's define it below.\n</div>","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    For each node, until the number of samples to process within the node is greater than the <b>minimum samples to split</b> number: <br>\n     <ol>\n         <li>Find each feature's best split. If there are $ k $ distinct values within a numeric feature, the number of possible splits among them is $ k - 1 $. Find the split which minimizes the <b>splitting criterion</b>.</li>\n      <li>Among these best splits (which will be 1 for each feature), find the best. Once again, according to the <b>splitting criterion</b>.</li>\n      <li>Split the node according to the best split found above. This will create 2 new nodes. Repeat the process until the conditions defined above are not valid anymore.</li>\n    </ol> \n</div>","metadata":{}},{"cell_type":"markdown","source":"<a id=\"4\"></a>\n# 🪓 Mean Squared Error | Splitting Criterion","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    The splitting criterion we'll be using to build our tree is the Mean Squared Error. Given a real output value $ y_i $ and a predicted output value $ \\hat{y}_i $, where $ i $ iterates over the $ n $ samples, the Mean Squared Error $ MSE $ is defined as follows:\n    $$ MSE = \\frac{1}{n} \\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2 $$\n</div>","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    So basically, we take the squared difference of our real outputs and our predicted outputs, sum them all up, and divide by the number of samples. <br>\n    Our objective is to minimize the MSE in our leaf nodes. <br>\n    In the best case scenario $ \\hat{y}_i = y_i $ for each $ i $, which means that each prediction is equal to the real output, which in turn means $ MSE = 0 $.\n</div>","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    In our House Prices example, $ y_i $ will be the house's real selling price, and $ \\hat{y}_i $ will our tree's predicted selling price. <br>\n    Now we can start building our Decision Tree Regressor.\n</div>","metadata":{}},{"cell_type":"markdown","source":"<a id=\"5\"></a>\n# 🧱 Building the Tree","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    Now we have all we need to start building our tree, so let's go through the steps of the CART algorithm using our example dataset. <br>\n</div>","metadata":{}},{"cell_type":"code","source":"df","metadata":{"execution":{"iopub.status.busy":"2022-10-02T10:29:56.395337Z","iopub.execute_input":"2022-10-02T10:29:56.396042Z","iopub.status.idle":"2022-10-02T10:29:56.409904Z","shell.execute_reply.started":"2022-10-02T10:29:56.395994Z","shell.execute_reply":"2022-10-02T10:29:56.408864Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"5.1\"></a>\n## CART Step 1","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    Step 1 will be, for each feature, finding the value that splits our outputs into 2 groups of data, according to the $ MSE $. <br>\n    For the NewConstruction and Pool features, there is only one possible split, since there are only 2 values in each feature. <br>\n    The split value will be the mean of the 2 values. Since our 2 values are 0 and 1, our split will be at 0.5. <br>\n</div>","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    Once we've grouped our real $ y_i $ into 2 groups, for each group, we calculate our prediction $ \\hat{y}_i $ to be the mean of the values within the group. <br>\n    Then, we calculate the $ MSE $ using these 2 $ \\hat{y}_i $ as our predicted values. <br>\n    Let's clarify this process below by looking at the NewConstruction feature.\n</div>","metadata":{}},{"cell_type":"markdown","source":"![2-1.jpg](attachment:b86056ba-7c7f-41cb-a5c1-871528bb97df.jpg)","metadata":{},"attachments":{"b86056ba-7c7f-41cb-a5c1-871528bb97df.jpg":{"image/jpeg":"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"}}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    Since we can only split the NewConstruction feature one way, we have that the minimum $ MSE $ for this feature is 2101.75.\n    $$ min(MSE_{NewConstruction}) = 2101.75 $$\n</div>","metadata":{}},{"cell_type":"code","source":"df","metadata":{"execution":{"iopub.status.busy":"2022-10-02T10:29:56.411878Z","iopub.execute_input":"2022-10-02T10:29:56.412836Z","iopub.status.idle":"2022-10-02T10:29:56.425105Z","shell.execute_reply.started":"2022-10-02T10:29:56.412777Z","shell.execute_reply":"2022-10-02T10:29:56.423855Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    Let's also compute the $ MSE $, by hand, for the Pool feature, which can only be split one way, on 0.5.\n</div>","metadata":{}},{"cell_type":"markdown","source":"![2-2.jpg](attachment:c155c0c7-c9a3-45ce-a9ed-aaf15e2cc0b6.jpg)","metadata":{},"attachments":{"c155c0c7-c9a3-45ce-a9ed-aaf15e2cc0b6.jpg":{"image/jpeg":"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\n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    Since we can only split the Pool feature one way, we have that the minimum $ MSE $ for this feature is 7164.5.\n    $$ min(MSE_{Pool}) = 7164.5 $$\n</div>","metadata":{}},{"cell_type":"code","source":"df","metadata":{"execution":{"iopub.status.busy":"2022-10-02T10:29:56.427002Z","iopub.execute_input":"2022-10-02T10:29:56.427848Z","iopub.status.idle":"2022-10-02T10:29:56.441796Z","shell.execute_reply.started":"2022-10-02T10:29:56.427792Z","shell.execute_reply":"2022-10-02T10:29:56.440532Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    For the LotArea, we have 5 distinct values, which means we have 4 ways to split the feature. <br>\n    The values are, in sorted order: [1533, 10140, 11924, 13695, 25419]. <br>\n    We calculate the 4 possible split values by taking the mean of each 2 adjacent data points. <br>\n    Let's write some code to assist us in this task.\n</div>","metadata":{}},{"cell_type":"code","source":"def get_split_values(df, feature):\n    distinct_values = sorted(df[feature].unique())\n    \n    split_values = []\n    for i in range(1, len(distinct_values)):\n        split_val = (distinct_values[i-1] + distinct_values[i]) / 2\n        split_values.append(split_val)\n    \n    return split_values","metadata":{"execution":{"iopub.status.busy":"2022-10-02T10:29:56.444448Z","iopub.execute_input":"2022-10-02T10:29:56.446527Z","iopub.status.idle":"2022-10-02T10:29:56.455415Z","shell.execute_reply.started":"2022-10-02T10:29:56.446472Z","shell.execute_reply":"2022-10-02T10:29:56.453591Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"split_values = get_split_values(df, 'LotArea')\nsplit_values","metadata":{"execution":{"iopub.status.busy":"2022-10-02T10:29:56.457265Z","iopub.execute_input":"2022-10-02T10:29:56.458047Z","iopub.status.idle":"2022-10-02T10:29:56.472756Z","shell.execute_reply.started":"2022-10-02T10:29:56.458004Z","shell.execute_reply":"2022-10-02T10:29:56.471555Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    Our possible split values for LotArea are [5836.5, 11032.0, 12809.5, 19557.0]. <br>\n    This means that we will compute 4 MSEs for LotArea and pick the smallest one, to get the best split for LotArea. <br>\n    Since the task is pretty time intensive by hand, let's write some code to assist us.\n</div>","metadata":{}},{"cell_type":"code","source":"# computing key split info given a feature and a split value\n# this is the code implementation of what we did before by hand\ndef get_split_info(df, feature, split_val):\n    df1 = df.loc[df[feature] <= split_val]\n    df2 = df.loc[df[feature] > split_val]\n    \n    y1_values = df1['SalePrice'].values\n    y1_pred = y1_values.mean()\n    \n    y2_values = df2['SalePrice'].values\n    y2_pred = y2_values.mean()\n    \n    mse = 0\n    for y1 in y1_values:\n        mse += (y1 - y1_pred)**2\n    \n    for y2 in y2_values:\n        mse += (y2 - y2_pred)**2\n    \n    n1 = len(y1_values)\n    n2 = len(y2_values)\n    n = n1 + n2\n    \n    mse /= n\n    \n    return {\n        \"split_value\": split_val,\n        \"y1_pred\": y1_pred,\n        \"n1\": n1,\n        \"y2_pred\": y2_pred,\n        \"n2\": n2,\n        \"mse\": mse\n    }","metadata":{"execution":{"iopub.status.busy":"2022-10-02T10:29:56.479393Z","iopub.execute_input":"2022-10-02T10:29:56.484619Z","iopub.status.idle":"2022-10-02T10:29:56.498086Z","shell.execute_reply.started":"2022-10-02T10:29:56.484553Z","shell.execute_reply":"2022-10-02T10:29:56.496655Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"split_info = []\nfor split_val in split_values:\n    split_info.append(get_split_info(df, 'LotArea', split_val))\nsplit_df = pd.DataFrame(split_info)\nsplit_df","metadata":{"execution":{"iopub.status.busy":"2022-10-02T10:29:56.499830Z","iopub.execute_input":"2022-10-02T10:29:56.500446Z","iopub.status.idle":"2022-10-02T10:29:56.534780Z","shell.execute_reply.started":"2022-10-02T10:29:56.500400Z","shell.execute_reply":"2022-10-02T10:29:56.533330Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"split_df.loc[split_df['mse'] == split_df['mse'].min()]","metadata":{"execution":{"iopub.status.busy":"2022-10-02T10:29:56.536842Z","iopub.execute_input":"2022-10-02T10:29:56.537389Z","iopub.status.idle":"2022-10-02T10:29:56.556388Z","shell.execute_reply.started":"2022-10-02T10:29:56.537341Z","shell.execute_reply":"2022-10-02T10:29:56.554935Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    As we can see, out of our 4 split values for LotArea, the one with smallest MSE is the split at 5836.5, with MSE of 4138.4.\n    $$ min(MSE_{LotArea}) = 4138.4 $$\n</div>","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    And with this, we are done with step 1 of the CART Algorithm, for the first node in our Decision Tree Regressor.\n</div>","metadata":{}},{"cell_type":"markdown","source":"<a id=\"5.2\"></a>\n## CART Step 2","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    For our 3 input features, we've calculated the best split according to the MSE.\n    $$ min(MSE_{NewConstruction}) = 2101.75 $$\n    $$ min(MSE_{Pool}) = 7164.5 $$\n    $$ min(MSE_{LotArea}) = 4138.4 $$\n    \n</div>","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    For Step 2, we find the smallest MSE of these, and so we pick the best feature, with the best split. <br>\n    Our best feature is NewConstruction, with a split value of 0.5, and an MSE of:\n    $$ MSE_{best} = 2101.75 $$\n</div>","metadata":{}},{"cell_type":"markdown","source":"<a id=\"5.3\"></a>\n## CART Step 3","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    And now for Step 3, our final step in our first iteration, we split the data according to the best split, thereby creating 2 new nodes, in which we can repeat the CART steps.\n</div>","metadata":{}},{"cell_type":"markdown","source":"![3-1.jpg](attachment:c81b28a8-14cd-4303-a4e0-f55f66450282.jpg)","metadata":{},"attachments":{"c81b28a8-14cd-4303-a4e0-f55f66450282.jpg":{"image/jpeg":"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djz7w6ra746iuJBkNM1w2fQcj+lex15t8MLLdcX18RwqrEv48n+Qr0gnAJ9K782mnX5FtFJHNgo2p8z6nkXhr978RFYc/6RM3/oVevV5H4G/e+Nw/tM38/wDGvXKrN/40V2S/UWB/ht+ZleI9XXRNDuLzI8wDbED3c9P8fwrzXwPpDax4h+1XALw2582Qn+JyeB+fP4Va+I2r/a9WTTo2zFajL47uf8BXaeDNI/sjw9Crrief97L9T0H4Cto/7Jgub7U/y/r8yH+/xFukToaKKK8Q9AKKKKACiiigAooooAKKKKACiquo6jbaVZSXl3JshTqcZJ9gKNO1C21SxjvLSTfDJ0OMH3BFVyS5ee2ncXMr8t9S1RRRUjPL/iZfGXVbWxU5EMe9h/tN/wDWH6133h+xGm6BZWuMMkQLf7x5P6mvL7w/258QyhOUe8Ef/AVOP6V7FXrY/wDdYelR8rv+vvOHDe/VnU+QUUUV5J3BRRRQAUUVFPcQ2sRluJUijBxudgo/WmlfRBsS0UisrqGUgqRkEHg0tIAooooAKKKKACuQ8TeOYdFuWsrWDz7tcbixwiZ/Umuvrx/xXpeo6L4jm1BVdonl86KcruAJ5we3HvXoZbRpVqtqnbRdzlxdScIXgep6ReTahpNtdzwGCWVAzRntXD/FCb5tNh/33/kKxl+IuvBcGS2Y+pi/+vWLrGuX2uzRzXzozRqVXYu0AZzXpYTLatLEKrK1tTkr4uE6XIr3PWvBaeX4Q04Y6xlvzJNb1eWad4d8X3tpCFvJLW2CDyw05AC9sBa9K0+Ca1063guJzPNGgV5T/EfWvLxtKMZuSmm23ouh2YeblFJxasizRRRXCdIUUUUAFFFFABRRRQAUUVWv7+20yyku7uTy4YxljjNNJydluJtJXZZrK8Snb4a1I/8ATu/8qxbP4iaTdaibd0kt4MEieUgAn3HaneJ/EWlXPhm/ittQt5ZXi2qivknJFdlPCVoVYqUXujCVenKDsznfhh/yFr7/AK4D/wBCr0+vKvh1fWdjqN19quY4mmVI4gx+8cn/AOtXqtbZsn9Zb9PyIwTXsUFFNeRIkLyOqIoyWY4ArPTxBpEkwhTUrVpGbaFEg5PpXnRhKWsVc6nJLdmlRRRUjCiiigAooooAKKKKACiiigAooooAKKKKACiikJABJOAO9AC0Vmw6/pNxqAsYb+GS5PRFbOfx6VpVUoSj8SsJSUtmFFFFSMKKx/EfiCDw9pxuJBvmc7Yov7zf4VgeF/HM2taothd20UTOpKPGx5I5xg+1dMMJWnSdWK91GUq8IzUG9Tt6KKK5jUKKKKACiiq2oXiafp9xdurMsKFyq9TjtTSbdkJuyuyzRXmM3xPvWb9xp9ui/wC25Y/piq7/ABM1dlwltZqfXDH+teispxT6L7zleOo9z1aivLLO98beIZQYJpoYs58zYIkH445r1CIOsKLIwZwoDMBjJ7mufE4V4eylJN+XQ1pVva3aTSH0UUVymwUUUUAFZuu6vDomkzXsuCVGI0/vMegrSJAGT0ryLxZrE3ibX47GxzJBG/lwqP42PBb/AD2rtwOG9vVs/hWrOfE1vZQ03ew7wnpM3ibxDJqF9mSGN/MlJ6O/Zf8APYV630rN0LSItE0mGyjwSozI395j1NaVLHYn29W6+FaIMNR9nDXd7hRRRXGdAUUUUAeafFBj9u09ewjc/qK7XwyoTwxpqj/n3T+VcR8UP+QhYf8AXFv513Phz/kW9N/690/lXrYn/cKXq/1OGj/vMzUoooryTuCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK8v+J//ACGLH/r3P/oVeoV5d8Tj/wATqyHpbn/0I16WU/70vmcmN/gs7nwp/wAirpv/AFwWtisfwoMeFdN/64LWxXHiP4svV/mdFL4I+gUUUViWFed/E+8+WwsgepaVh+g/rXolePePLk3vi2aJDnylSFR79f5mvTymnzYlPsrnJjZWpW7nc+ALP7L4VgcjDXDNKfoTgfoK6O6fy7SZ/wC7Gx/So9PthZadbWyjAiiVPyFRazJ5WiX8n923c/8AjprkqT9rXcu7NoR5KaXZHmfw5Xf4oZ/7sDn8yK9P1S+j0zTLi9l+7ChbHqew/OvNvhkudeuT6W3/ALMK1/iXqnlWVtpiN80zeZJ/ujp+v8q9TG0fb49U/T7jjw8/Z4ZyOR8OWUniDxVEJ8uGkM85PcA5P64Fe1jgVwXwz03y7K61J1+aVvKQ/wCyOv6/yrva5s1rc9fkW0dDXBU+WnzPdhVDV9ZstEszc3ku1eiqOWc+gFX68l1eabxh41Syhc/Z0cxIR0VR95v8+1YYLDKvN87tFas1xFZ04+7u9jqtM+IWn6jfw2jW08DStsVmwRk9M4rsK8VW1gl8bRWmmrsgW6WOMg5OFIy2fwJr2qtcxw9Ki4umrXV7GeFqzqJ8/QKKCcDJrG0rxRpes3s1paTMZYsnDLjcB1K+tcMacpJyitFudLlFNJvc2aK8/wDFGuahL4ustJ0y5kiKOgfYeGYnPPqAK6TxZrH9i+H550bE8n7uL/ePf8Bk1u8JNezXWexmq8fe/umHrXxDj0/VTaWlss8cT7ZpGbH1C/T1rtYpVmhSVDlHUMp9jXgv9nXD6V/aGx2jefyQQCcnGTXtsc6aZ4fjmn+VLe2UtnjovSu3MMJSoxgqW+qfmc+Frzm5Oex598R9Xa41OPTI3PlW6hpFHdz0/Ifzru/DNk2n+HLG2dQriIM49zyf515boNtJ4k8YRvONweUzzf7oOcfyFe0UZjajSp4ZdNX6/wBXDCXqTlVfoFQ3Uwt7SaY9I42f8hmpqxvFk/2fwrqMmcHySo/Hj+teXSjzzjHuzsnLli2ec+Aovtfi+OZxnYkkp+p4/rXr9eYfDCHOq302PuQqv5n/AOtXp9ejm8r4m3ZI5cCrUr9woooryzsEZlRSzMFUDJJOABXL3fxA0K1uGhEk0+3q8KZX8+9YvxF8QOjLo1s5UFQ1wR3B6L/X8qd4e+H1pNpiXGreaZpV3LGjbdgPTPvXqUsJRp0VWxLeuyRxzrVJVHTpdNzsNK1zTtaiMljcLJt+8h4ZfqK5n4mW9xLo9tMhXyIZcyA9cngf1rK1LwXqegXS6joNw8gQ52kgOo9+zCtXxLqMeq/D6ScTQySgxiXymyFfIyKulRhTr06tCV4t2816kzqSnTlCorO33lr4d3b3PhnynYsYJWjGey8Efzrra4T4YNnS79fScH/x2pvGvi99KP8AZ2nuBdsMySdfLB7D3/lWWIw0quMlTprqXSrKGHUpHZNNErhGkQMeACwyafXhmo6Vqmn29rqN8XRrli0ZZyXBHOT6V694b1CTVPD1neSkGV0+cjuQcH+VTi8CqEFUjLmT0+Y6GJ9pJxaszVrO1vWIND0yS9nBYLwqA8ux6CtGvOPihcN5unW38GHk+p4FY4Kgq9eNN7GmIqOnTckZb/EPXnnMyeQsQbPliLIA9Cetd74a8Q2/iXTmLRqs6cTQnkfUZ6g1X8IaXaDwdbRPCki3SF5Qwzuz6/hWDF4P1jQ/E0Nxo8oNm7jczNjameVYd69Cs8JW5qcUoSjs+9jlp+2hyzb5k9zuG0nTmOWsLUn1MK/4V5D4k23vjG4hhVVTz1gRUGAMYHb3zXs08qwW8kzfdjUsfoBmvGvC8Tap4ztXYZ3TNO34ZajKpNKpVk/hX9fkGNSfLBdWezxoIokjXooAH4U6iivFPQCq95fWun27XF3OkMS9Wc4//XWd4n1waBo0l0ArTMQkSt0LH19h1ry+e/1PxrrNpbSsgY/IiqMIvdmxXoYTASrr2knaC3Zy18Sqb5VrI6+++JllGStlZzTnoGc7Af611WiajJqukW97NbNbvICTG3bnr9Ko6N4R0rRo1KQLNcDrPKMtn29K3qzxM8M1y0I/N/5F0o1b3qP5AeBk0da4b4k6rLa2FtYQuV+0EtIQcHavb8z+lWPAul6pZ232m7vfMtJ4VaGEsW255zz04o+qWw/t5StfZdw9ver7NI7Gis3XdVGi6PPfmLzTGBhM4yScdaXQtVXWtHgv1j8syA7kznaQcHmuf2U/Z+0tpe3zNeePNydS+7rGjO7BUUZJPQCubk1rw94qSTRvtRczZAG0qSRzlSRWP8R9caC3j0mByGlG+fB/g7D8f6VB4W8EzxyaZrEl15ZH71odvOOwz9Otd9HC04UPb1Zcrfw/I5alaUqns4K66lhvhfaFvk1KdR6GNTWJ4o8G2vh7SlukvJZpHlCKrKAO5J/SvWK8/wDihNi106AH70juR9AB/WtcFjcRVrxhKWnyIxGHpQpuSWpS8A+HLHU7aTULyJpHhnAjG4gcAHkd+a9Nrlvh9D5XhOFsf62R3/XH9K6muTMKsqmIld6J2RvhYKNJW6mdrmkrrekTWDStEJMYdecEHPTuK88uPhxq9s4ktJ7afaQV5KH9eK9Ouru3sbZ7i6lWKFBlnY4Apba6gvLZLi2lWWFxlXU5Bow+Mr4eHufD6aBVoU6sve3GWK3K2EC3jK1yIwJWXoWxzViiqA1vSzdm1GoW3nhtpj8wZz6Vy2lNtpG11FWbL9FFcn4m8b22hzG0t4xc3YHzDdhY/r7+1VRozrS5KauxVKkaa5pM6yiuD8P/ABCbUNSSz1C3ih80hY5IycbvQ59a7wkAZPAqq+HqUJctRCp1YVFeLCimo6yKGRgynoQcinVgaBRRRQAUUUUAFFFVNS1G30rT5r25bbFGMn1J7Ae5pxi5Oy3E2krsNS1O00mza6vJRHGv5sfQDua8s1zxZqfiS5FlZJJHbudqQR8vJ/vY/l0qleXuqeM9cVEUszHEUQPyxr6/4mvTfDfhaz8P24KgS3bD95ORz9B6CvaVOjl8VOp71R7Lt/X/AAx57nPEvlhpEwvCvgR9PuYNR1GXE8Z3JAnRT7nufpXd0UV5eIxFSvPnqM7aVKNKPLEKKKqapdiw0q6uyceTEzj6gcVjGLk0kW3ZXZ5Z4oupvEfjEWUDEokgt4gOnX5j+efyqnpt/baR4wS7lglgt4JCvl4yyjG3/wCvWl8O7Q3niSS7k58iMvk/3mOP8a7zVfCWkavLLPcW5FxIu0yoxBGOh9M19HWxVLDS+rSXu8ttPM8qnRnVXtVvc0dP1Kz1S1FxZTpNGe69j6Edqt140GvvA3ijZvLopBYDgSxn29f6ivY43Ekauv3WAI/GvIxmFVBqUHeMtmd2Hre0TUlZrcdRRRXEdAV5b401u61jWxolkW8mOQRFFP8ArJPf2FepV4tqKX/hrxVLdGElknZ4nlUlXBzg5/GvVymEZVJS+0lp6nFjZNQS6Pc9A0TwLpemwI11Ct3dYyzyDKg+wrfTTbCIgx2VshHdYlH9K82X4namB81pZk/8CH9amh+KF4HHnafbsuedjkHH41dXA4+o3KWvzJhicNFWX5HptFZOheIbHxBbGW1YrInEkT/eX/63vWtXlThKEnGSs0dsZKSvHYKKKKgoKKKhu7qKys5rmZtscSF2PsKaTbsgbtqcp4+8Q/2bp39n274urkYYg8onc/j0/OqHw68P+XEdZuU+ZwVtwR0Xu349K5mwt7nxp4sZ5iQkjeZKR/BGOgH6CvY4okghSKJQsaKFVR0AFeximsJh1ho/FLWX9f1+JwUU69V1Xsth9FFFeMd4UUUUAFFFFAHmvxQT/StOfsUcfqK7Lwq+/wALaa3/AEwUflXLfFFR5OmN33yD9BXR+Czu8Iad7Rkf+PGvWr65fTfm/wBTip6Yqa8v8jeoooryTtCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK8p+Jj58QwL/dth/wChGvVq8i+Iz7vFBH923QfzNepk6vifkzjxz/dfM9J8Nps8Naav/Tun8q1Kp6Snl6NZJ6QIP/HRVyvPqu9ST82dUFaKQUUUVmUISACScAV4xpoOteO43Iyst2ZT/ugk/wAhXqviK8+weHb+4BwywsF+p4H8689+Gtn52uz3RHFvDgH/AGmOP5A17GX/ALvD1a3lb+vwODFe/VhTPVax/FT+X4W1Jv8Apgw/PitisDxq23whqPugH/jwrzcMr1oLzX5nXVdqcvRnG/DEf8Ti9Ppbj/0KsLxVqJ1jxJczRncgbyoh6gcD8zT/AA9qn9kWesTocSvbrFH/ALzN1/AZNO8GaYdT8S2ysuYoD50mfQdP1xX1DgqdapiZdF+h4/M504Ul1Z6zoenjS9FtLMDmOMBv97qf1rQoor5OUnKTk92e3FKKsjI8T6l/ZXh67uQcSbNkf+83Ary3QrwaPpeoakD/AKTIPsttnrk8s34DH511XxPvStpY2Sn/AFjmRh7AYH864CytbnVLq2sLdSzs21F7DPUn/Pavostw8fqrlLZu79EeVi6j9tZdPzZ1/wAN9IafUZdVkX93ACkZPdz1P4D+den1R0jTIdH0uCyg+7GvLf3j3P51erxcbiPrFZz6dPQ9DD0vZU1Ea43IwHcYrwzSLttK8S21wSQYrjD/AEzg17rXkvxA0i30zVoZrWPy0uVZ3GeN+ecfnXdlE480qMvtL/M58dF2U10JvDr/AG74mTTyHdiSZx+GQKb8RNTa91yPT4juS2GCB3kb/wCtgVmeGtUj0zxQL25+VCshP4rkf0qfwnaya94xW5nG4K7XMufXPA/MivSnSVOt7eW0I6ficcZ89P2a3kz0/QdLTStDtLLaNyIC/HVjyT+dcn8R9bEdvHo8LfPJiSbHZew/E8/hXW65rVvoWmvdzkE9I488u3YCvJtKsbvxd4kJmYnzG8y4k/ur6f0FeZl9Lnm8VW+Fa/M7MVPliqMN2dn8ONHNrpsupyriS5O2PPZB/if5V3FMhhjt4EhiULGihVUdgKfXn4ms69V1H1OqlTVOCigrl/iDL5fhKcf89JEX9c/0rqK434lPt8ORJ/euV/QGrwKviYLzJxDtSl6Gf8Lo/wBzqUnq6L+hr0KuE+GC40q+b1nA/wDHa7utMyd8VP8AroRhFajEKbI6xxs7HCqCSfaory9trC2a4upkiiXqzHFefah41vtXubi20m2/0FYX853X5iuDlv8AZ9qyw+FqV9YrRdS6taNPfcwtJjbxL44SSYbklnMzg/3F5A/QCvZa8q+GaK2vXDHqttx+LCvQ9Z1yy0OzM93IAcfJGPvOfQCu7NFKeIjRgtkkkc+DtGk5y6sz/GurLpfh2cBgJ7geVGO/PU/gK4azt3i+GWoTNws10u36AgVUll1Pxz4hVQMD+Ff4YU9f89a7bxfYw6b4CazgGI4vLUe/zDmuiEFhVToP4pSTZlKTrOVTok0jn/Burx6J4Y1e8fBZZVEa/wB5yvAqHwXo8mv65Lqt9mSKF97Fv+Wkh5A+g6/lXJJJNLEtnHkq0u4IO7EYFe4aBpSaLotvZqBuVcyH+856mtcfJYaM5R+Kf4IjDRdZxT2icn8UT/ommj/po/8AIVs+ATnwja+zOP8Ax41y3xOuxJqdnaA/6qIu31Y/4Cuy8HWxtfCmnoerR7z/AMCJP9a4665cupp9X/mb09cVJrt/kbtcJ8StKluLK21GIFhbkpIAM4U9/wAx+td3SMoZSrAEEYIPevOw1d0KqqLoddWmqkHFnm3hTx1a6fpsWn6kjqsQ2xzINw2+hFdSvjjw6wH/ABMVH1Rv8Kqaj8PtGvpGlhEto7HJ8o/L+R6VwvibRdL0KUWtvfTXN31ZcALGPfHf2r1o0sFjKl43Un0/q5wueIoR1s0jvte8Q2F34R1Kewu0mwnlnb1BbiuV+Gdr5mtXVyRxDDtH1Y/4CsOPTrm38JXOoyZWG5ljijB/iAJJP04xTdN16bSNGu7azLJc3Ui5lU4KIB29yTXVDBqNCpSou93b8r/dqYyr81WM6i2R7hRWb4fju49Bs1vpWlufLBdm688gH6CtKvmpx5ZON72PXi7pM4z4lW8kvh+GZeVhnBf6EEfzrjvAUqReLrXecb1dR9Stet6hZRajYT2c4zHMhU+3vXiF5a3nh3WzEx2XFtIGR+x7g/Q172WzVbDTw/XX8TzcXF06savQ94orG8O+IrXxBYiSJgtwo/ewk8qf8PetmvCqU5U5OMlZo9GMlJc0djyn4ly7/EEEeeI7YfqTXpeloI9Js0HRYEH/AI6K8r+Ihz4rk9oE/ka9GvNZtdD8PQ3dw3SJQiDq7YGAK9XFwcsNQhFav/gHFQklWqSZzfxL1RUsrfTEYb5G82T2UdPzP8q2vA9q9l4TtfNG0ybpeewJyP0rzrTba78ZeKd9xkh23zsOiIOw/kK9U1yT7D4avWhAURWzBAO3GBTxcFRpU8It27sKEnOcq726Hlw3eKfHXOWjmuPyjX/6w/WvZFAVQoGABgCvLvhnbK+s3Vw3WGDC+2T/APWrs9X8YaRpAKvcCecf8soTuP4noKWZRnUrRoUlflQYRxjTdSb3Zv15l8UHP9o6enYRMf1/+tXoWmX8WqabBfQqyxzLuAYYIrz34oL/AMTKwb1hYfrWWVxccWk91f8AI0xjvQbXkdn4Sj8rwppq/wDTEN+fP9a2qy/DZz4a03H/AD7p/KtGV/Lid8Z2qT+VcNe7qy9X+Z0U9IL0PMfHOrz6vrkeiWZJjikCFR/HKf8AD/GvQtG0yPR9Jt7GM5ES8t6t1J/OvLvBYW98ZrdXDD5fMnJY4+b/ACa7TXfHmnaWDDaFby59Eb5F+rf4V6uMoT9zC0Veyu/U4sPUj71ab3Dx5rr6VpC29u+25uiVDA8qvc/0rynyCUixvaeU5RFGcj1+pNbPi7VJ9X1WCaaBoG+zx/um/hJ5/rXqNvZaZo+l2txcRwR/ZIAvnuBlRjnB+tdFOosBQguW8pGUovE1JO9kjzmw8W6/oM8dte+Y8QAzDcrhgvqD1rrtB8FW1rfy6leTC+aQ74d68DPOTnqa4bVrybxf4pH2eNtsrCKJe4Qdz+pr2WCFbe3jhT7sahR9AMVjmFR0YR5VyykveSLwsVOT5tUtjyf4gWg0/wATrPAoj82NZRt4+YHGf0FS+KvF0+qJFY2UjJAsQadlOC7YyR9BVr4oD/iZaef+mLf+hVy0UAg8PT3jD57iYW8Z9gNzn/0EV3YWMKlClUmrtaL8v0Oes5RqTjHZnafDC7leO+tGYmJCroD2JyD/ACFehVwPwwtitlf3RH35FQH6DP8AWu+rw8yt9anb+tD0cJf2MbhRRRXCdIUUUUAFec/E++k8yxsFbEZBmYepzgf1r0auG+ImhXN/DBqNrGZDbqVlVfvbeuR645ruy2UI4mLmc2LUnRaiWPh5pMVroYvyoM90Sd3ooOAP0zXY141oPjTUdCtxaqkdxbAkrHJwVz6EV0SfFFMDfpTZ/wBmYf4V14zL8VUrSmle/mY0MVRjTUW7HodFefn4ow9tKl/7/D/Cuv0XWrTXLBbq1b2dD1RvQ159bB16MeapGyOmnXp1HaLNGuZ8fTmDwlcgHHmskf5nP9K6auP+JJI8MoOxuE/kaeCV8RBPugxDtSl6FP4YW4XTr64xy8oQH2A/+vXeVxvw1x/wjk3r9pb+Qrodb1i30TTJbycj5RhE7u3YCtMcpVMXKK1d7EYdqNBNnm3xHmSbxMsaYLRwKrY9SScfqK9SsEMenW0bfeWJAc+oAryTw1p8/ifxSbm6y8av59w3Y88L+J/QV7HXRmVqcKeH6xWplhLylKr3CiiivJO4KZJFHMu2RFdfRhkU+igCp/Zenk5Njbf9+V/wqC70DSb2BoZtPtyrDGVjCkfQitKirVSad02S4Re6PHYBL4Q8cCJWLRpKEOf4429fz/SvYq8h8fMr+MWEZBZY4wcev+cV65Hnykz12jNenmXv06VV7ta/gcmE92U4LZMdRRRXknaFcv8AECV4vCVwEz87ojY9M11FU9U0231bTprK5GY5BjI6g9iK2w81TqxnLZNGdWLlBxXU5H4ZW0S6Vd3QwZXm2H1AA4/ma7qvK28H+J9Dumk0qYyKf44ZNpI91Nammz+PBfW6XEW6DePMMqpjb35HNeji8PGvUlWhUi0+7szloVXTiqcoM9AoooryDuCiiigAooooA4D4o/8AHppv/XR/5Cug8Ff8ihp/+4f/AEI1znxRb91pif7Uh/QV0vg1dvhDTR6x5/U16tb/AJF1P1f6nFT/AN6l6f5G7RRRXlHaFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV4547fzvGF0o7LGn/AI6P8a9jrxjxB/pXjy4Truu0T8iBXr5NpWlLsv8AI4cf/DS8z2K2Ty7WJP7qKP0qWgdKK8lu7udyCiiikBl+ItLfWdCubGJ1SSQDazdMg55/KqPg/wAOyeHtNlS4dHuJn3OU6ADgCuiorZV5qk6Kfut3M3Si58/UK57xwCfCF/jsqk/99CuhqC8tIb+zltbhN8MqlWX2pUZqnUjN9GmOpHmg4rqfPue3avWvh/op07RzeTIVnuyGwRyEHT8+tQWPw3sLW/W4mu5biJG3LCygA+gJ712wAAwOBXrZlmMK0PZ0tnv/AJHFhMLKEueZV1K8Gn6Zc3hXd5MbPgnGcDpXl+k+O7621O4vNQllnikU7bdcBQe2M9AK9XliSeJ4pUDxuCrKwyCDXMy/D7QJJS4glQH+BJSFFcmCrYaEZRrxvc2xFOrKSdN7HmviDX7nxDfC5uESNUXbGifwjr17mvR/BHhyDS9Njv2IkurmMNvx9xTyFH9a43x1olto1/aJZwGO3eHGeTlgTnJ9elaVn8RFsdCt7SKyZ7qGJY9zsNnHGeOa9XExnXwsI4Ve6+nkcVJxp1pOs9UeiXl7bafbtcXc6QxL1ZziuF1T4jSTTfZtDtDI7Haskikkn2Uf1rmIY9b8a6rhpGlIOWY8Rwj6dv516doPhiw0CEeSnmXBHzzuPmP09BXDKhh8Gv33vT7dPn/XyOlVatd+5pHuV/CcfiAQTy65JkyENFG2Ny+ucdPpWP8AE+33abY3AH+rmKE/Uf8A1q7uuY8fwed4SuGA/wBU6Sfrj+tc+Frc2MjOyV3027Gtanag43ueX3dmv9iWGoI4y7PBIvfcvIP5EVd8N+Jm8OLcmKzSaWbA3u5G0DtgVitPI1tHb5yiMzKPc4/wFeu6b4L0UaVardafHJceUvmOSQS2Oehr3sZWpUafLXV02/8AM82hTnUnenpY86Z9Z8Z6uODNJ0AHEcS/0H6mvVPDvh+38P6cII/nmf5ppcfeP+FaFpY2thD5Vpbxwx/3UUCrFeFi8c60VTguWC6Ho0MMqb5pO8gooorzzqCuK+Jik6DbEdBcDP8A3ya7Ws/W9Ih1vS5bGZiofBVx1Vh0NdGFqqlWjOWyZlWg503FHIfDW7t4dJvUlnjjYTbiHYDjaOeau658QbCwDQ6eBeXHTcDiNfx7/hXI3Hw+16KcpHBFMm7AdZAAR64PSul0D4dwWzLcau63Eg5EKfcH19a9avDBe0dec+a/Rf1/kcNKWI5VTjG1upz9npmveOLwXN5MyWgP+sYYRR6Ivc16LaeHbCw0eXTraIKksZR3PLOSMZJrURFjQIihVUYCgYAFOrzsRjZ1bRj7sVskddLDxhq9W+p4iINd8Lak4iSaCcqU3qm4Oue3GDVuw8N694muxcXXnLGx+a4uc9PYHk17HRXXLN5tXjBKXcwWBjezk7djM0TQrPQbIW9qnJ5kkb7zn1P+FQ+KtOl1Tw3eW0ClpioZFHcg5xWzRXmKtNVFVbu73Ox048nItjy3wR4Wu5NXS/vrZ4YLY5VZVKl37YB7CvUqKK0xWKniZ88iKNGNKPKjxnxotxP4yu4yjM7MqxqBywwMYr1Hw3pzaVoFpaSbvMVMuCc4Y8kVotbwvKsrRRmRfuuVBI+hqStcTjXWowpJWUSKWH9nOU273CqbarYLqK6ebqIXbDIi3fNWR401ybQ9F8y2wLiZ/LRj/Dxkn614/HcTidpEdzO+RvySxz1x7mtsFlrxEHUk7LoRiMX7KXKlc9K8WeOY7ISWOlOslz915hysf09T/Kud8KeEp9euPt9/vFlu3EsfmmPt7eprQ8L+AXnKXusIUi6pbHq3+96D2r0lESJFRFCoowFUYAFa1cTSwkHRw2r6y/r+kZwozry9pV26Iy9b0OLVtCk0xCsIwPKIXhCOnHpXMaF8OzZajHdajcRTrGdyxIpwW7ZJrvaK8+njK1Om6cXozqnQpzkpNaoKKKK5jYK5/wAUeF4PENoMFYryP/VykfofaugorSlVlSmpwdmiZwjOPLLY8k0rwv4m0vXbeSG2aMpIAZQ42Fe+eeRivW6KK2xWLniWpTSTXYzo0I0U1Fnl/wAStMmj1SHUQpaGWMRkgfdYev1Brn4k1nxXewwrvuDGojU4wkSj17Cvb2RXUq6hlPYjIpkUEUClYYkjUnJCKAP0rso5q6dJQ5byWzOeeC55uV9GZfh3w/b+H9PEEWHmbmWXHLn/AArQvrRL6wntJPuTRlD7ZFWKK82VWcp+0k9TsUIqPKloeSt4A8Q29w0UDRNGePNWbaCPcda6PQvh3aWZWfVHF1MOREOI1Pv6129FdlXNMRUjy3t6HPDB0ou9riKiogRFCqBgADAArkPH2gz6tYQXFnC0txbsRsXqynr+uK7CiuWhWlRqKpHdG9SmqkXFmX4ct7i18O2EF0nlzxxBWX0/yK02UMpU9CMGlorOc3OTl3HGPKkjy/Ufhtfpet/Z9xDJbsSQZGKsvsfWt/w54CtdLdLq/Zbm6XlVx+7Q/Tua7Giu2pmWInDkbMI4SlGXMkebePfDd9Nqn9p2cEk8ciASLGMlCvGcemK58WXifX5I4JY72cL087Kov54Fe00VrSzSdOmocqbWzIngoyk5Xtc5rwp4Th8PwmaVhLeyLh3HRB6L/jXS0UV59WrOrNzm7tnTCEYR5Y7HM+LfCjeIxbPDcLDNDkfOuQVP0+lcX4205dGtNH02M7o4onYvjG5yRk161WH4k8M23iOCJZZXhliJKSKM9eoIruwWOdOcI1H7iv8Aic+Iw6nGTiveZyHhnxhpWgeH4bWRJ5Zy7PII04GT6k+mK7fQNbi1/TftsMUkS7yhV/Ue/euUsfhlBHOWvr5poweEjXZn6n/Cu7ggitYEggjVI0G1VUcAUY+eFk26V3Ju7YYaNZK09EiSiiivNOsKKKKAGTTR28LzSsFjjUszHsB1rO0bxBp+vRytYyM3lEBw67Tz0P0pfEMU8/h6/itl3TNAwVfXivKfC/iZvDU1wTa+ekwAYb9pGM/416OFwX1ihOUfiWyOWtiPZVIp7M9P1DwpompSGS4sI/MPV48oT+VZ/wDwr3w//wA8Zv8Av8ayR8UYO+lS/wDf4f4Uh+KMWeNKkx7zD/CtY4fMYq0b/f8A8EzdXCt3dvuNyPwF4ejOTZM/+9Kx/rWtpWjWOi27wWMPlo7bmyxJJ+prP8O+LLLxDvjjVoLhOTE5BJHqD3rfrixE8Qm6dZv0bOilGk1zQSCsHxjpcmreG7iGFd0yESovqR2/LNb1FY0qjpzU47o0nFTi4vqeM+HPF1z4cgnt0tknjdt2HYqVboajuLrWvGmqom0yMDhUQYjiHqfT616teeHNHv5/OutOgkk/vFcE/XHWrlnYWmnw+TZ28cEfXai4r2JZnQTdWnT999f6/wCAcKwlRrklL3Sj4e0KDQNMW2iw0jfNLJjl2/w9K1qKK8ac5Tk5Sd2zujFRXKtjOutc06y1KDT7i5VLmf7iYPOemT2zWjXBeONC1K41iy1PTYXmdQFKqOVYHIP0ruoi5hQygCQqNwHQHvW9alTjThODu3v5Mzpzk5yjJbD6y/EOpvpGhXV9GEMka/IH6Ek4FalUdY01NX0m5sZDgSpgNjO09j+dZUnFVIue19TSd+V8u5S8Ka1JruiJdTeWJwxSQJ0yPbtxVjXdctdC09rm4YbiCI4+7t6CvLH0nxN4cuZVt0u0HeW2yUcevFLbeHfEfiK8D3KXH+1PdZAA9s9foK9iWX0HUdV1Fyb/APAOBYqpy8nK+Yf4asrnxN4s+13A3Isnnzt268D88flXsVZmh6HaaDYLa2y5J5kkPV29TWnXBjsUsRU934Vojpw1F0o67vcKKKK4joCiiigAooooAKKKKACiiigAooooA80+KEn+nafH6RO35kf4V23hqPyvDWmp6W6H8xmvPviZJv1+BP7tsP1Jr0zTI/K0qzj/ALsKD/x0V62K93BUY+v9ficVHXETZaoooryTtCiiigAooooAKKKKACiiigCr9ouf+fJ/+/i/40faLn/nyf8A7+L/AI1aooAq/aLn/nyf/v4v+NH2i5/58n/7+L/jVqigCr9ouf8Anyf/AL+L/jR9ouf+fJ/+/i/41aooAq/aLn/nyf8A7+L/AI0faLn/AJ8n/wC/i/41aooAq/aLn/nyf/v4v+NH2i5/58n/AO/i/wCNWqKAKv2i5/58n/7+L/jR9ouf+fJ/+/i/41aooAq/aLn/AJ8n/wC/i/40faLn/nyf/v4v+NWqKAKv2i5/58n/AO/i/wCNeQ25a88fq4jLM1+W2ZGeG6Z/CvZ2YKhY9AM14/4Mj+1+N4ZD2aSU/kf8a9fLPdp1p9l/mcOM1lCPmer/AGi5/wCfJ/8Av4v+NH2i5/58n/7+L/jVqivIO4q/aLn/AJ8n/wC/i/40faLn/nyf/v4v+NWqKAKv2i5/58n/AO/i/wCNH2i5/wCfJ/8Av4v+NWqKAKv2i5/58n/7+L/jR9ouf+fJ/wDv4v8AjVqigCr9ouf+fJ/+/i/40faLn/nyf/v4v+NWqKAKv2i5/wCfJ/8Av4v+NH2i5/58n/7+L/jVqigDI1SzXWLJ7S8013jbkESKCp9Qc8GucT4f6arqzWt+4ByVNwmD+Vd1RW9PE1qS5YSaRnOjTm7yVzN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id=\"5.4\"></a>\n## Minimum Samples Split","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    How do we decide when to stop splitting a node, and keep a node's prediction as the mean of the samples within the node? <br>\n    We do that by setting a Minimum Samples Split, which is the minimum number of samples within a node to start the split finding steps 1 through 3. <br>\n    Let's say in our case, we set the Minimum Samples Split to 4. That means that we can't split the right node anymore, but we can split the left node. \n</div>","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    Let's go through steps 1 through 3 in our left node, using the code we defined above.\n</div>","metadata":{}},{"cell_type":"code","source":"df_left = df.loc[df['NewConstruction'] < 0.5]\ndf_left","metadata":{"execution":{"iopub.status.busy":"2022-10-02T10:29:56.559091Z","iopub.execute_input":"2022-10-02T10:29:56.560368Z","iopub.status.idle":"2022-10-02T10:29:56.578764Z","shell.execute_reply.started":"2022-10-02T10:29:56.560320Z","shell.execute_reply":"2022-10-02T10:29:56.577253Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"split_values = get_split_values(df_left, 'Pool')\nsplit_info = []\nfor split_val in split_values:\n    split_info.append(get_split_info(df_left, 'Pool', split_val))\nsplit_df = pd.DataFrame(split_info)\nsplit_df","metadata":{"execution":{"iopub.status.busy":"2022-10-02T10:29:56.580723Z","iopub.execute_input":"2022-10-02T10:29:56.581146Z","iopub.status.idle":"2022-10-02T10:29:56.603585Z","shell.execute_reply.started":"2022-10-02T10:29:56.581112Z","shell.execute_reply":"2022-10-02T10:29:56.602197Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"split_values = get_split_values(df_left, 'LotArea')\nsplit_info = []\nfor split_val in split_values:\n    split_info.append(get_split_info(df_left, 'LotArea', split_val))\nsplit_df = pd.DataFrame(split_info)\nsplit_df","metadata":{"execution":{"iopub.status.busy":"2022-10-02T10:29:56.605827Z","iopub.execute_input":"2022-10-02T10:29:56.606400Z","iopub.status.idle":"2022-10-02T10:29:56.631014Z","shell.execute_reply.started":"2022-10-02T10:29:56.606352Z","shell.execute_reply":"2022-10-02T10:29:56.629311Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    The split with the lowest MSE is splitting the LotArea at 5836.5. <br>\n    We split the left node, and this is the resulting tree.\n</div>","metadata":{}},{"cell_type":"markdown","source":"![3-2.jpg](attachment:33212d6c-01f6-482e-bcad-8f0ac99c5cc8.jpg)","metadata":{},"attachments":{"33212d6c-01f6-482e-bcad-8f0ac99c5cc8.jpg":{"image/jpeg":"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\n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    And now, since all leaf nodes contain less samples the the Minimum Samples Split number, our tree is complete! <br>\n    In the next section we'll see how we'd do all this with just a few lines of code, with the help of scikit-learn.\n</div>","metadata":{}},{"cell_type":"markdown","source":"<a id=\"6\"></a>\n# 💻 Code Implementation","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    sklearn's DecisionTreeRegressor will be doing all the heavy lifting, but now that we've seen how it works, we perhaps have a deeper appreciation of the open source community.\n</div>","metadata":{}},{"cell_type":"code","source":"# defining our tree object with the hyper parameters discussed above\nmodel = DecisionTreeRegressor(\n    criterion='squared_error',\n    splitter='best',\n    min_samples_split=4,\n)\n\n# CART algorithm that builds the tree based on our data\nmodel.fit(\n    X=df.drop(columns=['SalePrice']), \n    y=df['SalePrice']\n)","metadata":{"execution":{"iopub.status.busy":"2022-10-02T10:29:56.632767Z","iopub.execute_input":"2022-10-02T10:29:56.633174Z","iopub.status.idle":"2022-10-02T10:29:56.650528Z","shell.execute_reply.started":"2022-10-02T10:29:56.633121Z","shell.execute_reply":"2022-10-02T10:29:56.649183Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    sklearn's plot_tree comes in handy so that we can visualize our Decision Tree Regressor.\n</div>","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(10, 10))\nplot_tree(\n    model,\n    feature_names=['NewConstruction', 'Pool', 'LotArea'],\n    impurity=False,\n    filled=True,\n    fontsize=18\n)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-10-02T10:29:56.652139Z","iopub.execute_input":"2022-10-02T10:29:56.652855Z","iopub.status.idle":"2022-10-02T10:29:57.170047Z","shell.execute_reply.started":"2022-10-02T10:29:56.652812Z","shell.execute_reply":"2022-10-02T10:29:57.168249Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    So now, if for example we were to feed in a house with NewConstruction = 0, Pool = 1 and LotArea = 7000, we'd use the predict method to derive the SalePrice.\n</div>","metadata":{}},{"cell_type":"code","source":"inputs = pd.DataFrame([{\"NewConstruction\": 0, \"Pool\": 1, \"LotArea\": 7000}])\nprediction = model.predict(inputs)\nprint(f\"Predicted SalePrice: {prediction}\")","metadata":{"execution":{"iopub.status.busy":"2022-10-02T10:29:57.172042Z","iopub.execute_input":"2022-10-02T10:29:57.172569Z","iopub.status.idle":"2022-10-02T10:29:57.187857Z","shell.execute_reply.started":"2022-10-02T10:29:57.172520Z","shell.execute_reply":"2022-10-02T10:29:57.185917Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"7\"></a>\n# 🙇 Conclusion","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    With this our notebook has come to an end. <br>\n    If there are any mistakes, please point them out to me in the commets, this is a living and breathing document. <br>\n    For related notebooks: <a href=\"https://www.kaggle.com/code/chazzer/ml-grandmaster-decision-tree-classifier/\">🌳 Decision Tree Classification</a>, <a href=\"https://www.kaggle.com/code/chazzer/ml-grandmaster-random-forest/\">🌳 Random Forest</a>, <a href=\"https://www.kaggle.com/chazzer/ml-grandmaster-gradient-boosting-xgboost/\">🌳 Gradient Boosting (XGBoost)</a>.\n</div>","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #e8f4ff;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    Stay tuned for more notebooks in the ML Grandmaster series. <br>\n    I hope you find this series of notebooks useful/fun!\n</div>","metadata":{}},{"cell_type":"markdown","source":"## ML Grandmaster Notebooks\n<br>\n<div\n     style=\"float: left; position: relative; width: 100%;\"\n     >\n    <table\n           style=\"float: left; font-size: 16px\"\n           >\n      <tr>\n        <th><b>📈 Machine Learning Fundamentals</b></th>\n        <th><b>🌳 Decision Trees</b></th>\n      </tr>\n      <tr>\n        <td><a href=\"https://www.kaggle.com/chazzer/ml-grandmaster-linear-regression/\">Linear Regression</a></td>\n        <td><a href=\"https://www.kaggle.com/code/chazzer/ml-grandmaster-decision-tree-classifier\">Decision Tree Classification</a></td>\n      </tr>\n      <tr>\n        <td>Logistic Regression [work in progress]</td>\n        <td><a href=\"https://www.kaggle.com/code/chazzer/ml-grandmaster-decision-tree-regressor\">Decision Tree Regression</a></td>\n      </tr>\n      <tr>\n        <td></td>\n        <td><a href=\"https://www.kaggle.com/code/chazzer/ml-grandmaster-random-forest/\">Random Forest</a></td>\n      </tr>\n      <tr>\n        <td></td>\n        <td><a href=\"https://www.kaggle.com/chazzer/ml-grandmaster-gradient-boosting-xgboost/\">Gradient Boosting (XGBoost)</a></td>\n      </tr>\n    </table>\n</div>","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: white;\n            font-size: 250%;\n            text-align: center;\n            display: fill;\n            border-radius: 5px;\n            background-color: #66023C;\n            overflow: hidden;\n            font-weight: 700;\n            border: 5px solid #F28C28;\"\n     >\n    🏆 Machine Learning Grandmaster\n</div>","metadata":{}}]}