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    "# Lecture 5: Logistic Regression and Decision Trees\n",
    "In the previous lecture, we learned about linear regression, which explores a __linear__ relationship between the independent and dependent variables. In a sense, logistic regression is analogous to linear regression in that it is a generalized linear model. However there are several key differences in logistic regression that makes it very different. Let us explore those differences, and understand how logistic regression works\n",
    "## Categorical Classification\n",
    "One key difference between logistic regression and linear regression is that the final output of the logistic regression model is binary, that is, 0 or 1, whereas linear regression has no such property. Thus a logistic regression model will always map from the real number space to a binary space of 0 and 1. Let us examine how logistic regression does this.\n",
    "### The Logistic Equation\n",
    "The key underlying equation that underlies the model is the __logistic equation__ and is formulated as below:\n",
    "![image](https://wikimedia.org/api/rest_v1/media/math/render/svg/5e648e1dd38ef843d57777cd34c67465bbca694f)\n",
    "\n",
    "In this case, the t in the equation is some linear combination of n variables, or a linear function in an n-dimensional feature space. The formulation of t is therefore identical to the linear regression formula. \n",
    "\n",
    "To summarise the logistic equation:\n",
    "1. Takes an input of n variables\n",
    "2. Takes a linear combination of the variables as parameter t\n",
    "3. Using the parameter, outputs a value that always lies between 0 and 1\n",
    "\n",
    "A visualization of the outputs of the logistic equation is as below (note that this is but one possible output of a logit regression model):\n",
    "![image](https://upload.wikimedia.org/wikipedia/commons/8/88/Logistic-curve.svg)"
   ]
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    "### Threshold Value\n",
    "It's important to realize that the logistic regression should output a __binary__ set of numbers, namely 0 and 1. While the logistic equation does have an output between 0 and 1, the output is continuous. So how do we convert it to 0 and 1?\n",
    "\n",
    "We use something called a threshold value, such that if the output of the F(x) > threshold, then 1 otherwise, 0. As a general formula:  \n",
    "![image](https://wikimedia.org/api/rest_v1/media/math/render/svg/aab892e7cf0d00aa6da3aa051335900ff52d12a0)\n",
    "\n",
    "The threshold value is the epsilon value in the equation, and is a key parameter in logistic regression, because it determines two key characteristics of a logistic regression classifier: \n",
    "1. __Sensitivity__\n",
    "2. __Specificity__\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sensitivity and Specificity\n",
    "\n",
    "__The Confusion Matrix__\n",
    "![image](http://rasbt.github.io/mlxtend/user_guide/evaluate/confusion_matrix_files/confusion_matrix_1.png)\n",
    "\n",
    "The confusion matrix is a good representation of the predictive power of a logistic regression model.\n",
    "\n",
    "__Sensitivity__, otherwise known as a __True Positive Rate__, is the proportion of true positives out of the entire pool of \"actual positives.\"   \n",
    "\n",
    "    The formula is True Positive / ( True Positive + False Negatives )  \n",
    "    \n",
    "__Specificity__, otherwise known as a __True Negative Rate__, is the proportion of true negatives out of the entire pool of \"actual negatives.\"   \n",
    "\n",
    "    The formula is True Negative / ( True Negative + False Positives )\n",
    "    \n",
    "It is important to understand that there will always be a trade-off between the two characteristics. This trade-off is best understood in terms of how we set our threshold values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC curve\n",
    "Let us consider the trivial cases:classify everything as the same value.\n",
    "If we classify all points as positive, __sensitivity = 1 and specificity = 0__. All positive data points have been classified as positive, along with all the negative data points.  \n",
    "On the contrary, if everything was classified as negative, __sensitivity = 0 and specificity = 1__ All negative points have been classified correctly. \n",
    "\n",
    "Sensitivity __decreases__ as threshold grows, since the predictor will classify more and more positive points incorrectly.\n",
    "Specificity __increases__ as threhold grows, since the predictor will classify more and more negative points correctly.\n",
    "\n",
    "This trade-off is represented by the ROC curve, which tells us how good a model performs in terms of specificity and sensitivity.\n",
    "Sample ROC curve\n",
    "![image](https://www.statsdirect.com/help/resources/images/ebx_1266835018.gif)"
   ]
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     "text": [
      "    Sepal.Length Sepal.Width Petal.Length Petal.Width is_setosa\n",
      "1            5.1         3.5          1.4         0.2         1\n",
      "2            4.9         3.0          1.4         0.2         1\n",
      "3            4.7         3.2          1.3         0.2         1\n",
      "4            4.6         3.1          1.5         0.2         1\n",
      "5            5.0         3.6          1.4         0.2         1\n",
      "6            5.4         3.9          1.7         0.4         1\n",
      "7            4.6         3.4          1.4         0.3         1\n",
      "8            5.0         3.4          1.5         0.2         1\n",
      "9            4.4         2.9          1.4         0.2         1\n",
      "10           4.9         3.1          1.5         0.1         1\n",
      "11           5.4         3.7          1.5         0.2         1\n",
      "12           4.8         3.4          1.6         0.2         1\n",
      "13           4.8         3.0          1.4         0.1         1\n",
      "14           4.3         3.0          1.1         0.1         1\n",
      "15           5.8         4.0          1.2         0.2         1\n",
      "16           5.7         4.4          1.5         0.4         1\n",
      "17           5.4         3.9          1.3         0.4         1\n",
      "18           5.1         3.5          1.4         0.3         1\n",
      "19           5.7         3.8          1.7         0.3         1\n",
      "20           5.1         3.8          1.5         0.3         1\n",
      "21           5.4         3.4          1.7         0.2         1\n",
      "22           5.1         3.7          1.5         0.4         1\n",
      "23           4.6         3.6          1.0         0.2         1\n",
      "24           5.1         3.3          1.7         0.5         1\n",
      "25           4.8         3.4          1.9         0.2         1\n",
      "26           5.0         3.0          1.6         0.2         1\n",
      "27           5.0         3.4          1.6         0.4         1\n",
      "28           5.2         3.5          1.5         0.2         1\n",
      "29           5.2         3.4          1.4         0.2         1\n",
      "30           4.7         3.2          1.6         0.2         1\n",
      "31           4.8         3.1          1.6         0.2         1\n",
      "32           5.4         3.4          1.5         0.4         1\n",
      "33           5.2         4.1          1.5         0.1         1\n",
      "34           5.5         4.2          1.4         0.2         1\n",
      "35           4.9         3.1          1.5         0.2         1\n",
      "36           5.0         3.2          1.2         0.2         1\n",
      "37           5.5         3.5          1.3         0.2         1\n",
      "38           4.9         3.6          1.4         0.1         1\n",
      "39           4.4         3.0          1.3         0.2         1\n",
      "40           5.1         3.4          1.5         0.2         1\n",
      "41           5.0         3.5          1.3         0.3         1\n",
      "42           4.5         2.3          1.3         0.3         1\n",
      "43           4.4         3.2          1.3         0.2         1\n",
      "44           5.0         3.5          1.6         0.6         1\n",
      "45           5.1         3.8          1.9         0.4         1\n",
      "46           4.8         3.0          1.4         0.3         1\n",
      "47           5.1         3.8          1.6         0.2         1\n",
      "48           4.6         3.2          1.4         0.2         1\n",
      "49           5.3         3.7          1.5         0.2         1\n",
      "50           5.0         3.3          1.4         0.2         1\n",
      "51           7.0         3.2          4.7         1.4         0\n",
      "52           6.4         3.2          4.5         1.5         0\n",
      "53           6.9         3.1          4.9         1.5         0\n",
      "54           5.5         2.3          4.0         1.3         0\n",
      "55           6.5         2.8          4.6         1.5         0\n",
      "56           5.7         2.8          4.5         1.3         0\n",
      "57           6.3         3.3          4.7         1.6         0\n",
      "58           4.9         2.4          3.3         1.0         0\n",
      "59           6.6         2.9          4.6         1.3         0\n",
      "60           5.2         2.7          3.9         1.4         0\n",
      "61           5.0         2.0          3.5         1.0         0\n",
      "62           5.9         3.0          4.2         1.5         0\n",
      "63           6.0         2.2          4.0         1.0         0\n",
      "64           6.1         2.9          4.7         1.4         0\n",
      "65           5.6         2.9          3.6         1.3         0\n",
      "66           6.7         3.1          4.4         1.4         0\n",
      "67           5.6         3.0          4.5         1.5         0\n",
      "68           5.8         2.7          4.1         1.0         0\n",
      "69           6.2         2.2          4.5         1.5         0\n",
      "70           5.6         2.5          3.9         1.1         0\n",
      "71           5.9         3.2          4.8         1.8         0\n",
      "72           6.1         2.8          4.0         1.3         0\n",
      "73           6.3         2.5          4.9         1.5         0\n",
      "74           6.1         2.8          4.7         1.2         0\n",
      "75           6.4         2.9          4.3         1.3         0\n",
      "76           6.6         3.0          4.4         1.4         0\n",
      "77           6.8         2.8          4.8         1.4         0\n",
      "78           6.7         3.0          5.0         1.7         0\n",
      "79           6.0         2.9          4.5         1.5         0\n",
      "80           5.7         2.6          3.5         1.0         0\n",
      "81           5.5         2.4          3.8         1.1         0\n",
      "82           5.5         2.4          3.7         1.0         0\n",
      "83           5.8         2.7          3.9         1.2         0\n",
      "84           6.0         2.7          5.1         1.6         0\n",
      "85           5.4         3.0          4.5         1.5         0\n",
      "86           6.0         3.4          4.5         1.6         0\n",
      "87           6.7         3.1          4.7         1.5         0\n",
      "88           6.3         2.3          4.4         1.3         0\n",
      "89           5.6         3.0          4.1         1.3         0\n",
      "90           5.5         2.5          4.0         1.3         0\n",
      "91           5.5         2.6          4.4         1.2         0\n",
      "92           6.1         3.0          4.6         1.4         0\n",
      "93           5.8         2.6          4.0         1.2         0\n",
      "94           5.0         2.3          3.3         1.0         0\n",
      "95           5.6         2.7          4.2         1.3         0\n",
      "96           5.7         3.0          4.2         1.2         0\n",
      "97           5.7         2.9          4.2         1.3         0\n",
      "98           6.2         2.9          4.3         1.3         0\n",
      "99           5.1         2.5          3.0         1.1         0\n",
      "100          5.7         2.8          4.1         1.3         0\n",
      "101          6.3         3.3          6.0         2.5         0\n",
      "102          5.8         2.7          5.1         1.9         0\n",
      "103          7.1         3.0          5.9         2.1         0\n",
      "104          6.3         2.9          5.6         1.8         0\n",
      "105          6.5         3.0          5.8         2.2         0\n",
      "106          7.6         3.0          6.6         2.1         0\n",
      "107          4.9         2.5          4.5         1.7         0\n",
      "108          7.3         2.9          6.3         1.8         0\n",
      "109          6.7         2.5          5.8         1.8         0\n",
      "110          7.2         3.6          6.1         2.5         0\n",
      "111          6.5         3.2          5.1         2.0         0\n",
      "112          6.4         2.7          5.3         1.9         0\n",
      "113          6.8         3.0          5.5         2.1         0\n",
      "114          5.7         2.5          5.0         2.0         0\n",
      "115          5.8         2.8          5.1         2.4         0\n",
      "116          6.4         3.2          5.3         2.3         0\n",
      "117          6.5         3.0          5.5         1.8         0\n",
      "118          7.7         3.8          6.7         2.2         0\n",
      "119          7.7         2.6          6.9         2.3         0\n",
      "120          6.0         2.2          5.0         1.5         0\n",
      "121          6.9         3.2          5.7         2.3         0\n",
      "122          5.6         2.8          4.9         2.0         0\n",
      "123          7.7         2.8          6.7         2.0         0\n",
      "124          6.3         2.7          4.9         1.8         0\n",
      "125          6.7         3.3          5.7         2.1         0\n",
      "126          7.2         3.2          6.0         1.8         0\n",
      "127          6.2         2.8          4.8         1.8         0\n",
      "128          6.1         3.0          4.9         1.8         0\n",
      "129          6.4         2.8          5.6         2.1         0\n",
      "130          7.2         3.0          5.8         1.6         0\n",
      "131          7.4         2.8          6.1         1.9         0\n",
      "132          7.9         3.8          6.4         2.0         0\n",
      "133          6.4         2.8          5.6         2.2         0\n",
      "134          6.3         2.8          5.1         1.5         0\n",
      "135          6.1         2.6          5.6         1.4         0\n",
      "136          7.7         3.0          6.1         2.3         0\n",
      "137          6.3         3.4          5.6         2.4         0\n",
      "138          6.4         3.1          5.5         1.8         0\n",
      "139          6.0         3.0          4.8         1.8         0\n",
      "140          6.9         3.1          5.4         2.1         0\n",
      "141          6.7         3.1          5.6         2.4         0\n",
      "142          6.9         3.1          5.1         2.3         0\n",
      "143          5.8         2.7          5.1         1.9         0\n",
      "144          6.8         3.2          5.9         2.3         0\n",
      "145          6.7         3.3          5.7         2.5         0\n",
      "146          6.7         3.0          5.2         2.3         0\n",
      "147          6.3         2.5          5.0         1.9         0\n",
      "148          6.5         3.0          5.2         2.0         0\n",
      "149          6.2         3.4          5.4         2.3         0\n",
      "150          5.9         3.0          5.1         1.8         0\n"
     ]
    }
   ],
   "source": [
    "# Preprocess iris to create a binary case \n",
    "iris_mod <- iris\n",
    "iris_mod <- dplyr::mutate(iris_mod, is_setosa = as.numeric(Species=='setosa'))[-5]\n",
    "print(iris_mod)\n",
    "\n",
    "# Split train, test\n",
    "ind <- sample(nrow(iris_mod),0.8*nrow(iris_mod))\n",
    "train <- iris_mod[ind,]\n",
    "test <- iris_mod[-ind,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Call:\n",
      "glm(formula = is_setosa ~ Sepal.Width, family = binomial, data = train)\n",
      "\n",
      "Deviance Residuals: \n",
      "    Min       1Q   Median       3Q      Max  \n",
      "-2.6015  -0.5468  -0.2140   0.2627   1.9871  \n",
      "\n",
      "Coefficients:\n",
      "            Estimate Std. Error z value Pr(>|z|)    \n",
      "(Intercept)  -21.228      3.967  -5.351 8.76e-08 ***\n",
      "Sepal.Width    6.468      1.237   5.227 1.72e-07 ***\n",
      "---\n",
      "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "(Dispersion parameter for binomial family taken to be 1)\n",
      "\n",
      "    Null deviance: 146.607  on 119  degrees of freedom\n",
      "Residual deviance:  78.476  on 118  degrees of freedom\n",
      "AIC: 82.476\n",
      "\n",
      "Number of Fisher Scoring iterations: 6\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Use glm function to predict species just with Sepal.Width\n",
    "fit_logit <- glm(data=train,family=binomial,formula = is_setosa ~ Sepal.Width)\n",
    "print(summary(fit_logit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "roc.default(response = test$is_setosa, predictor = pred_logit)\n",
       "\n",
       "Data: pred_logit in 16 controls (test$is_setosa 0) < 14 cases (test$is_setosa 1).\n",
       "Area under the curve: 0.6987"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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x/qLg6vdtP4ghGSN+ccFvXl5gjhequEn+7uJ82pWbY3IflpH67bbajr1bOvHx6/\nfpm5C+v271m/34yCIEJyZ9feoaG63EnvcvOeVfuGdLmzYPdwf72j02NIj1+/3Wuh/WOs2cVH\nSB7tN90de+5uJ3h314anu0j8fnj8+uWzbbtSt2fNLj5C8unQvovMD6l7g9qwZhcfIfnyW8Nd\nGo+VjArpVJ/XDhes2cVHSL6swmWK+7qt0046dDMGq/B7yM/ywzbS8s020vmdbXlgzU4BIfmy\nD2HbnP+zbIO6zdp1U3Xnh+dNnlX7Ydlc7nr7GNLj128HPCxCxZqdAkJypr5uC7V7jM4f24fd\nresv97qtjqfH/UX3Hx6+vgiXO7XvAnN2GgjJm8O6OgfU3eW826W6uB7OsD2ncd1Xe45tdT2C\n4f7Dw9f3i0tITWDNTgMhOSZxvNzuw0GtkEVIjkmEtAyDTm3CTITk2PyQbofnITZCcmx+SNVl\npgLRERIggJAAAYQECCAkQAAhAQIICRBASIAAQgIEEBIggJAAAYQECCAkQAAhAQIICRBASICA\n/wHechxw3anXmgAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/svg+xml": {
       "isolated": true
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# predict using predict\n",
    "pred_logit <- predict(object = fit_logit, newdata = test)\n",
    "# use roc function in pROC library\n",
    "library(pROC)\n",
    "roc_curve <- roc(predictor = pred_logit, response=test$is_setosa )\n",
    "plot(roc_curve)\n",
    "auc(curve)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Area Under Curve \n",
    "The area under the ROC curve should always be greater and equal to the total proportion of the majority class, since the worse case is classifying everything as one. The closer to 1 the area is, the stronger the model strength"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.698660714285714"
      ],
      "text/latex": [
       "0.698660714285714"
      ],
      "text/markdown": [
       "0.698660714285714"
      ],
      "text/plain": [
       "Area under the curve: 0.6987"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "auc(roc_curve)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Decision Trees\n",
    "Linear and logistic regression aren't the only ways to make predications, we can also use a method called cllassifcation and regression trees, or CART.\n",
    "## CART\n",
    "CART builds what is called a tree by splitting on the values of the independent variables. To predict the outcome for a new observation or case, you can follow the splits in the tree and at the end, you predict the most frequent outcome in the training set that followed the same path. Some advantages of CART are that it does not assume a linear model, like logistic regression or linear regression, and it's a very to interpret how the model works.\n",
    "\n",
    "Let's make a simple CART model. We'll be attempting to predict supreme court decisions, as mentioned in lecture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-2-cff4912f83d6>, line 8)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-2-cff4912f83d6>\"\u001b[0;36m, line \u001b[0;32m8\u001b[0m\n\u001b[0;31m    SupremeCourtTree = rpart(Reverse ~ Circuit + Issue + Petitioner + Respondent + LowerCourt +\u001b[0m\n\u001b[0m                                     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "install.packages(\"rpart\")\n",
    "library(rpart)\n",
    "install.packages(\"rpart.plot\")\n",
    "library(rpart.plot)\n",
    "TrainCourt = read.csv(\"resources/TrainCourt.csv\")\n",
    "TestCourt = read.csv(\"resources/TestCourt.csv\")\n",
    "\n",
    "SupremeCourtTree = rpart(Reverse ~ Circuit + Issue + Petitioner + Respondent + LowerCourt + \n",
    "                         Unconst, data = Train, method=\"class\", minbucket=25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above code, we're trying to predict the value of Reverse (whether or not the supreme court will reverse a lower court's decision) using the Circuit, Issue, Petitioner, Respondent, LowerCourt, and Uncost features, all found in the Train dataset.\n",
    "\n",
    "The minbucket parameter controls how many splits are made in our tree by setting the minimum number of observed data points in each branch of the tree. If it’s too small, overfitting will occur (variance).If it’s too large, model will be too simple and inaccurate (bias). You'll learn more on bias vs. variance in future lectures.\n",
    "\n",
    "It's very easy to visualize our CART model, and can be done with the prp function. This is one of the reasons CART is more interpretable than Logisitc regression. We can see exactly how it works.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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xTrhFk1O+jOcJ0o7blkz3zqFHt53azt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\nqxTivicqleM/T/m5HpBmXf5r40/D7zxvleCw68Y9knbZZRezl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2+ba3LOOeeULavcSd5/9957r/Pee+8Zh0pwruRsv/32DkznQ7MF78Ok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\nAQhAAAIQgAAEqkTg/wNB+tGZlPa+iwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prp(SupremeCourtTree)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The predict() function allows us to apply our model and predict future cases. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "PredictCART = predict(SupremeCourtTree, newdata = TestCourt, type = \"class\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Terms to Review  \n",
    "1. Logistic Equation / Logistic function\n",
    "2. Confusion Matrix\n",
    "3. Sensitivity\n",
    "4. Specificity"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.3.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}