{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "## Learning Objectives\n", "\n", "Today we focus on two issues that may have bothered you in the past:\n", "\n", "1. Noise \n", "2. Error measure\n", "\n", "We explain what they are and why they are important to machine learning." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Review\n", "\n", "Now we know how to learn. We come up with a hypothesis set and we try all members of that hypothesis set on the training data. We then take the best one and we return it, calling it our final hypothesis. We know that this hypothesis has some probability of doing well on real world data based on what we have seen last time. \n", "\n", "This is all well and good, but you probably want to see this in action, so let's do it below:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "%matplotlib inline\n", "import seaborn as sns\n", "import numpy as np\n", "\n", "# here are our inputs\n", "# notice we have X and y\n", "X_train = np.array([0, 1, 2, 2, 3, 4, 5])\n", "y_train = np.array([0, 1, 3, 4, 5, 5, 8])" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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qEEdAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADAxLC/kns8OOcCkm6S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\nRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# remember we never just plot the whole distribution if we want a test\n", "sns.jointplot(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# here are our hypothesis set\n", "# notice these are functions\n", "h0 = lambda x: x**2\n", "h1 = lambda x: x + 2\n", "h2 = lambda x: x + 1" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.428571428571\n", "0.285714285714\n", "0.285714285714\n" ] } ], "source": [ "# now we test how accurate our functions are on the real data\n", "from sklearn.metrics import accuracy_score\n", "\n", "for h in [h0, h1, h2]:\n", " preds = map(h, X_train)\n", " print accuracy_score(y_train, preds)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "And now since h0 was the most accurate we return it and pat ourselves on the back. Right?\n", "\n", "Well there should be a couple of things that should feel off to you. First is that while the first hypothesis is the most accurate on the training data, it is highly unlikely that the true function is quadratic. So there seems to be something wrong with our error measure, accuracy.\n", "\n", "And second, we see something really strange in the training data in general. We have two different values for x = 2. \n", "\n", "Let's deal with the second issue first (it should be quick)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Noise \n", "\n", "We are finally call to bear a question that may have been bothering you since we first introduced supervised learning. And this is noise. The idea is that there is no perfect function out there that maps our data to the right outcomes. For example two people may have the exact same credit report and one will default and the other will pay back the loan. You can call it randomness (which I think is the right way to call it) or you can call it a lack of information (the credit report can't contian every facet of the person so we must account for as much by assuming noise). \n", "\n", "All this means is that the exact same X value can take on many y values in a dataset. And so if your mind we thinking r.v.'s then you were thinking right. The only difference is that the random function that we said exists now becomes a random function with noise. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Error Measures\n", "\n", "Coming back to the first issue, this is one that is due careful consideration. The error measure that we have been using thusfar has been that of accuracy (% right), but this often does not make sense as a measure. In cases where you are only off by a smidge you will be considered just a wrong as being off by a ton. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "One of my favorite examples here is that of the fingerprint scanner. There are two ways that it can be wrong. The first is that it could say yes when it should say no and the second is that it could say no when it should say yes. And you can imigine different situtations where one would be worse than the other. \n", "\n", "For example the FBI would weight letting somebody in when they are not supposed to be in, as something really bad! In that case having that happen would reduce the score by a ton, whereas having a good guy scan her finger twice wouldn't matter so much, so that would not be as penalized.\n", "\n", "There are many common error measures (in fact I have done another video on this), but let's explore two pretty common ones:\n", "\n", "* MSE (mean squared error): The means that for each point your loss is the square of the difference between your predicted and your true y value\n", "* MAE (mean absolute error): The means that for each point your loss is the absolute value of the difference between your predicted and your true y value\n", "\n", "These are common error measures. One believes that an error is an error and that all errors should have equal weight (mae) the other says the big erros should be counted more. Let's evaluate our hypotheses!" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ACC: 0.428571428571\n", "MAE: 4.71428571429\n", "MSE: 61.0\n", "\n", "ACC: 0.285714285714\n", "MAE: 1.0\n", "MSE: 1.57142857143\n", "\n", "ACC: 0.285714285714\n", "MAE: 0.857142857143\n", "MSE: 1.14285714286\n", "\n" ] } ], "source": [ "# now we test how accurate our functions are on the real data\n", "from sklearn.metrics import mean_squared_error, mean_absolute_error\n", "\n", "for h in [h0, h1, h2]:\n", " preds = map(h, X_train)\n", " print 'ACC:', accuracy_score(y_train, preds)\n", " print 'MAE:', mean_absolute_error(y_train, preds)\n", " print 'MSE:', mean_squared_error(y_train, preds)\n", " print" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Notice what happens, for the first error measure, h0 was the best, but for the other two h2 was the best one. \n", "\n", "A careful construction of the error measures to ensure that the error measure measures what you want your function to capture is paramount and will ultimately depend on your specific learning problem. The FBI will have different desires from a supermarket. And this is often one part of the data science curriculum that can't be taught." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Back to the assumptions\n", "\n", "let's view how our assumptions have changed:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "import networkx as nx\n", "from nxpd import draw\n", "from nxpd import nxpdParams\n", "nxpdParams['show'] = 'ipynb'\n", "\n", "c0 = 'Unknown target function, f(X) = y + noise'\n", "c1 = 'Inputs, (x_1, y_1), ..., (x_n, y_n)'\n", "c2 = 'Learning Algorithm'\n", "c3 = 'Hypothesis set, H'\n", "c4 = 'Final Hypothesis, g(X) = y'\n", "c5 = 'IID samples from a r.v. X'\n", "c6 = 'Train set'\n", "c7 = 'Test set'\n", "c8 = 'Reported Hypothesis Performance'\n", "c9 = 'Error Measure'\n", "\n", "G = nx.DiGraph()\n", "G.add_node(c0, color='orange')\n", "G.add_node(c1)\n", "G.add_edge(c0, c1)\n", "G.add_node(c2)\n", "G.add_node(c3)\n", "G.add_edge(c3, c2)\n", "G.add_node(c4)\n", "G.add_edge(c2, c4)\n", "G.add_node(c5)\n", "G.add_edge(c5, c1)\n", "G.add_node(c6)\n", "G.add_node(c7)\n", "G.add_edge(c1, c6)\n", "G.add_edge(c1, c7)\n", "G.add_edge(c6, c2)\n", "G.add_edge(c7, c8)\n", "G.add_edge(c4, c8)\n", "G.add_node(c9, color='green')\n", "G.add_edge(c9, c8)\n", "G.add_edge(c9, c2)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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vtz6//4Zik1ynutrn5P1ZDnVPbrNT7QuWfKdxwYuoosCSKkZS7Z7Ruvq2JRmd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qkgZaWq4SfBJ8FGktaXNE2SkSEQFQIjg5HB0pL6S/pFUqmIWTxmKPwgplTlWjmGQMwRGJZR51ym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"text/plain": [ "" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "draw(G)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "We edited the node in orange, we added the simple characters: + noise, but we added in the node in green which is the error measure. You can estimate any error measure and try to find the hypothesis that will minimize this error measure on real data, but what that error measure is you must choose. And quite the important choice it is! " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Next time\n", "\n", "Well congrats, we are done with the above diagram. These are all the assumptions that we are going to create, and you will see that this will cover a broad swath of research from statistics to machine learning. \n", "\n", "Next time we will talk about what happens when our hypothesis space is infinite and do a big review of all we have done by walking through another example in an infinite hypothesis space." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "## Learning Objectives\n", "\n", "Today we focus on two issues that may have bothered you in the past:\n", "\n", "1. Noise \n", "2. Error measure\n", "\n", "We explain what they are and why they are important to machine learning." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Comprehension Questions\n", "\n", "1.\tIs there a universal best hypothesis for a specific train and test set?\n", "2.\tHow do you choose your hypothesis set?\n", "3.\tWhat is the typical size of a hypothesis set?\n", "4.\tWhere does noise come from?\n", "5.\tWould noise exist if we could measure everything?\n", "6.\tWhat would be good error measures for self driving cars? For networks generating art? For detecting cancer?\n", "7.\tWhat is the error measure for humans?\n" ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }