{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# HIDDEN\n", "from datascience import *\n", "import matplotlib\n", "matplotlib.use('Agg', warn=False)\n", "%matplotlib inline\n", "import matplotlib.pyplot as plots\n", "plots.style.use('fivethirtyeight')\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Prediction ###\n", "\n", "An important aspect of data science is to find out what data can tell us about the future. What do data about climate and pollution say about temperatures a few decades from now? Based on a person's internet profile, which websites are likely to interest them? How can a patient's medical history be used to judge how well he or she will respond to a treatment?\n", "\n", "To answer such questions, data scientists have developed methods for making *predictions*. In this chapter we will study one of the most commonly used ways of predicting the value of one variable based on the value of another." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The foundations of the method were laid by [Sir Francis Galton](https://en.wikipedia.org/wiki/Francis_Galton). As we saw in Section 7.1, Galton studied how physical characteristics are passed down from one generation to the next. Among his best known work is the prediction of the heights of adults based on the heights of their parents. We have studied the dataset that Galton collected for this. The table `heights` contains his data on the midparent height and child's height (all in inches) for a population of 934 adult \"children\"." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Galton's data on heights of parents and their adult children\n", "galton = Table.read_table('galton.csv')\n", "heights = Table().with_columns(\n", " 'MidParent', galton.column('midparentHeight'),\n", " 'Child', galton.column('childHeight')\n", " )" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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... (924 rows omitted)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "heights.scatter('MidParent')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The primary reason for collecting the data was to be able to predict the adult height of a child born to parents similar to those in the dataset. We made these predictions in Section 7.1, after noticing the positive association between the two variables. \n", "\n", "Our approach was to base the prediction on all the points that correspond to a midparent height of around the midparent height of the new person. To do this, we wrote a function called `predict_child` which takes a midparent height as its argument and returns the average height of all the children who had midparent heights within half an inch of the argument." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def predict_child(mpht):\n", " \"\"\"Return a prediction of the height of a child \n", " whose parents have a midparent height of mpht.\n", " \n", " The prediction is the average height of the children \n", " whose midparent height is in the range mpht plus or minus 0.5 inches.\n", " \"\"\"\n", " \n", " close_points = heights.where('MidParent', are.between(mpht-0.5, mpht + 0.5))\n", " return close_points.column('Child').mean() " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We applied the function to the column of `Midparent` heights, visualized our results." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Apply predict_child to all the midparent heights\n", "\n", "heights_with_predictions = heights.with_column(\n", " 'Prediction', heights.apply(predict_child, 'MidParent')\n", " )" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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uWvKi4tJybrt7KTU1dTicdobn9sEAnA47GFBRXcPewmKSE1xs2b4HAJfTwfPL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\nb6e3vsL83gAUIwhU4Sr+GeXqNVhzV0b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UTT5AW+UMNCVwvNv/yJ93+96x72jiK6+q4adD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\nlVb22WQ0EB9naXJdgZMP+fMIGtufpiYDyslM13IbPB6Vyqo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prPZBOLGagjjS74rMpyCE\naEsyfBBDjdW6b2kN/NDPhatTECoxMZ7yymoUneLtKTCYtG1G6nr2by9wPN/j8WDw1T/w9xQYdTr2\nffc9N89b6qsdoMdmd6FXvD0FiuLNIfDfcQfmErjdwV/5iuIrkOT7oofwKYD+noK+vXPYX1SMw+HE\nbDZxVs8cIPIz/glxlqCegnD5Df5j518mMTn6Sa9amosQKvCcW612Dhw6rh3nB+beIPMpCCHalP6+\n++5b2NFBdCbl5eV06dKlRZ8dMaQfO3bvx+F0kdUllQfm3kBCvKXR15u7vtsmX0xu95wmP3PhqIH8\n98MvUD3eCYlGDutHj5xMXz6Bd0Igj6pSXWtlx+79TBg7ssH2EuLNnCyvIT7OTFpqMk88NIPPd+6n\n3mrDaNCTnpZMr9wsFEWhutZKr+6ZFJeU41Ehzmxm+YO/YOe+Q7icLlKSE3luyR18X1yGyajn2PGT\n2vbizEbGXjCM3rlZWp2ArhmpJCfFgwpxFhMOh8vbEDHoeXrhL7HaHKQkJzC0oA9PLZzFgcPHozqu\nvXOS2fbVgQYxBX52/I+G898Pv9CWeenJu8js0vbj9YG/c4HnvOjICbp1TdeO847d+3lg7g0t+l2K\nRWydTWeOTYjTleQUhOjM45Stje3meUtxOF3azyajIWxhnuauR/WoxFv02qQ+LS3kA8GJdBaTCUU5\nVTBp8pVjuHvxCy1KBAw9dp0pYa+x8xqr89UaP+TrQQjRkOQUnEFiWZo3cD0Hj5RQWl6Nw+ni2Ily\nbrvrSY6dKNd+bqxUcDj+3gyH08WWz/ewefsebT0/n7uM8spqXB4P5ZXV3HrnEy2KP3Q7zY2xvcS6\nbLYQQkQijYIzSKyS4ULX07tHVtCkPjW19S1OhgtMpHO63LgCJmGyO5xhEwFLSiuY99Cz3DxvKfMe\nerbRSYwa205nTdiL1fkSQohoSaLhGSRWyXCh6/FP/wveO9rExPiwRYqiEZhI55+Ayb9efw2D0ERA\n/12/Tqdod/2R9vN0SNiL1fkSQohoSaMghmI9Th1t8SJ/waLa2nrMFpM2eVDRkRP06ZlNTmZ6q2KJ\ntF+zpk7gpl8vod5ajN3hYnD/XmGLCfnXc+zESS22lKQEUMHm8OYNTLnyAu5a/Ly2L8MG9MGjer+0\nVy6eza8efMb7eKGicM6gvuz+poiNW3dgszsxGfX07d0tqrv+yVeO4edzl2G12QHvpE/zHnpWigEJ\nIc5oMnwQQ7Eepw5d35/WbAi73G13P6WNtReXnOS9TV+xt/Aw5ZXV7Pn2cKtjibRfq1/9N7k56ZjN\nRoxGPYePldK7Rzbds7sETX/sX09gbFu272HL56fyBu5e/Dy9e2QzdNBZ5PXpTlpKojaN8vubd5Cc\nFE96ahKpKYns/e4It971JG63B4/Hg83u5LuDx6K667978QsYjXp0eh0qKjv2HOy0uQVCCNFepFEQ\nQ7Eep462eFFg0R1V9U4e5HC60el03gqBrYwlmoI6iqJE3KZ/PYHLuVxubZrlSPkI5VU1uFzuU9UP\nXW5qa+vJP6s7FrMJvU6HXq+Lauy9NUWehBDih0oaBTEU62zx0PU1VrzIO4bvmyNA8U7+YzJ6Cw8Z\njYZWxxJpv9JTknzlj5vepn89gcsZDHotdyAwHyHctvyFklRV1WoXJCbGYzAYGNS/F0MHnsXY84dF\n1f3vP2aKTsGjejDo9Z02t0AIIdqLFC8K0RbFi1oq2uJF/oJFLqeL1NQkxowcQGpKIg6Hi7yzumuZ\n67V1Vh5Y8hJr3t7I+g+286/3tvH3f23iw607GTGkX6Ox9uyWwYuvv8eRoyeotzl4ZP60oKI+I4b0\nY+tnu0hJSQra5uQrxzDll4/y5Oo3eWnd+9w18xq+Ly7DbDZpy+X36U7PbpmoqkpWl1QemT+NwgNH\nwx7DEUP6sb+omJKyCoxGA+cPH8CS+29rdPlAoee1qSJPbVkMKJzOXIRHYhPizBJV8aKSkhIWLlzI\nhg0bqK2tpU+fPixfvpwxY8Zoyzz22GO8/PLLVFZWMmLECJYtW0ZBQUGbBt8WOnNBlNbGFjiZzq59\nRQAMLugdcWKdaCbhCRebf3KmlkwwFGs/5PPaliQ2Ic4sEYcPqqqqGD9+PIqisG7dOj799FOWLFlC\nZmamtsxTTz3FqlWrWLp0KRs3biQzM5Nrr72Wurq6Ng1eNE9TNQCaGktvaa5EayYYEkII0f4iPpL4\n9NNPk5OTwzPPPKO91rNnz6Blnn32We68804mTpwIwKpVq8jLy2PdunXccsstMQ5ZtFRTNQCaGktv\n6TP9rZlgSAghRPuL2Ch49913ufzyy5k+fTqbNm2ia9euTJs2jZkzZwJQVFRESUkJY8eO1T5jsVgY\nM2YM27Zt+8E0CqKpQdCcOgWxrGnw1vrNzLr3D7jcHnQKXHTeYJIS4xus1zsp0t84WVnDmBED0ekU\n6m12bbnGBH6uqWVD9+nJh2Zw18PPU1NbT6JvgqFwyzdWt0D1qOwpPIzVaicxMZ4nFsxg3bubtfX7\n50Koqq7DZndw9qA+9OmR0+BYlpRW8Ls/vqnNzRCrWgSdaf4EIYSIhYg5BV27dkVRFObMmcM111zD\nzp07mT9/PgsXLmTGjBl8+umnTJgwgZ07d9K9e3ftc7/+9a85fvw469ata/OdiKXGximjGVePZpmW\nLBsptsyh1+P0Paqnqt7TOf6SEVGvNxYKCwtZ+cr7zdon/zHYW3gIq81BnMWsxT+4oDebtu3C7XaT\nnJSAx+PB5XRz7vACbf3bvtiH0ainrt6Gy+3GZDRywahBDbbrr7iYmJgY02PSknMYTmceG5fYhDiz\nROwp8Hg8jBgxggULFgAwZMgQ9u/fz/PPP8+MGTNatfHCwsJWfb6thIvr8NFi7Xl6gEPf2xssF80y\nLVk2Umwutxvv1AOn2nf+fI5o1xsLzd0n//JWqx2PqmK12bxvqN74nS7vDIEu33+tdjtW66m8BJvd\njl5vwe3xoKDgdLmwWusbbPfw0WIURYn5MWnpOQyns14LILE1lzRUxOksYqMgOzub/Pz8oNfy8/P5\n05/+BEBWVhaqqlJaWhrUU1BaWkpWVlaT6+6MF09jdx89u+cE3RV2y0pvsFw0y7Rk2UixGfR6rafA\n3zBISEiIer2xUFhY2Ox98i8fF2du0FOQkJCA0WDA7XZjMBjweDxYzGbi4uK19VvMZnQ6HXqdt/iQ\nyWAkLi6+wXZ7ds8J6imI1TFpyTkMpzPf8UpsQpxZIj59MHr06Aat8cLCQnr06AFA7969yc7OZuPG\njdr7NpuNrVu3Mnr06BiH23GimbGuObPaxXIGvD8v+42WOKjXKYw9f0iHzKzX3H3yLz8gryddUpMZ\nmN+TC0YO5IKRAzEZDVw6Zhjdu2Zg0OlIT01mzYp7gta/ZsU9dElNJjkxgXiLhZFD+4Xd7gNzbyAr\nPTnmx0RmMRRC/NBEzCn48ssvGT9+PPfeey+TJk1ix44dzJ07l4ULFzJ9+nTA+4TCE088wcqVK+nb\nty/Lli3jk08+4bPPPiMhIaFddiRWOvPdh8TWcp05PomtZTpzbEKcriIOH5xzzjm8+uqrLFq0iGXL\nlpGbm8uCBQu0BgHAvHnzsNlszJ8/Xyte9Oabb552DQIhhBDiTBbV1Mnjxo1j3LhxTS5z7733cu+9\n98YkKCGEEEK0P5kQSQghhBBAlD0F4swmRXqEEOLMID0FIiJv1cFyHE4Xx06U8+iKv3V0SEIIIdqA\nNApERC2dEEkIIcTpRRoFIqL0lCQ8Hu+Tq82ZEEkIIcTpRRoFIiIp0iOEEGcGSTQUEWVlpLbLpEpC\nCCE6lvQUCCGEEAKQRoEQQgghfKRRIIQQQghAcgpiSor8CCGEOJ1JT0EMSZEfIYQQpzNpFMSQFPkR\nQghxOpNGQQxJkR8hhBCnM8kpiKEH5t7Aoyv+xsnKUzkFZzLJsRBCiNOLNApiSIr8BPPnWOh0ipZj\nIcdHCCE6Lxk+EG1GciyEEOL0Io0C0WYkx0IIIU4vMnwQQ5HG0M+0MXbJsRBCiNOLNApiKNIY+pk2\nxi45FkIIcXqR4YMYijSGLmPsQgghOjNpFMRQpDF0GWMXQgjRmUmjIIYemHsD3bPTMRkNdMtKbzCG\nHul9IYQQoiNJTkEMRRpDlzF2IYQQnZn0FAghhBACkEaBEEIIIXykUSCEEEIIQHIKfhDOtKJIQggh\n2ob0FPwA+IsiOZwurSiSEEII0VzSKPgBkKJIQgghYkEaBT8AUhRJCCFELERsFPz+978nLS0t6E9B\nQYH2/pw5cxq8f8UVV7Rp0CKYFEUSQggRC1ElGubn5/POO++gqt67Ub1eH/T+2LFjWb16tfa+0WiM\ncZiiKVIUSQghRCxE1SjQ6/VkZGQ0+r7JZGryfSGEEEJ0flHlFBw6dIgBAwYwbNgwfvGLX1BUVBT0\n/ieffEJeXh4jR45k3rx5lJWVtUWsQgghhGhDEXsKRo0axTPPPENeXh6lpaUsXbqU8ePHs23bNlJT\nUxk3bhxXX301vXr14vDhwyxevJirr76aDz/8UIYRhBBCiNNIxEbBZZddFvTzqFGjGDZsGGvWrGHO\nnDlce+212nv+3oQhQ4bwn//8h4kTJ8Y+YiGEEEK0iWZXNIyPj6egoIADBw6Efb9r165069at0fcD\nFRYWNnfz7aKzxgUSW2t05vgktpbpjLHl5eV1dAhCtFizGwU2m43CwkIuvvjisO+XlZVRXFxMdnZ2\nxHV1xounsLCwU8YFEltrdOb4JLaW6cyxCXG6iphouGDBAjZv3syhQ4fYvn07t9xyC/X19dx4443U\n1dWxYMECPvvsMw4fPsymTZu46aabyMrKkqEDIYQQ4jQTsafg2LFjzJw5k5MnT5K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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Draw the original scatter plot along with the predicted values\n", "\n", "heights_with_predictions.scatter('MidParent')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The prediction at a given midparent height lies roughly at the center of the vertical strip of points at the given height. This method of prediction is called *regression.* Later in this chapter we will see where this term came from. We will also see whether we can avoid our arbitrary definitions of \"closeness\" being \"within 0.5 inches\". But first we will develop a measure that can be used in many settings to decide how good one variable will be as a predictor of another." ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }