DataScienceWorkshop_full.ipynb

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    "outputId": "c16bf519-bad3-4d2b-c8e5-43a7819a76dc"
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     "text": [
      "--2019-05-26 17:05:55--  https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\n",
      "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n",
      "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 3974305 (3.8M) [application/x-httpd-php]\n",
      "Saving to: ‘adult.data.3’\n",
      "\n",
      "adult.data.3        100%[===================>]   3.79M  1.36MB/s    in 2.8s    \n",
      "\n",
      "2019-05-26 17:05:58 (1.36 MB/s) - ‘adult.data.3’ saved [3974305/3974305]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "e0glevjV_jfU"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "DNHss1Aq96QU"
   },
   "outputs": [],
   "source": [
    "column_names = [\n",
    "    \"Age\", \"WorkClass\", \"fnlwgt\", \"Education\", \"EducationNum\",\n",
    "    \"MaritalStatus\", \"Occupation\", \"Relationship\", \"Race\", \"Gender\",\n",
    "    \"CapitalGain\", \"CapitalLoss\", \"HoursPerWeek\", \"NativeCountry\", \"Income\"\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Kq4hC_0OXTvU"
   },
   "source": [
    "I am using [Pandas](https://pandas.pydata.org/) to read an manipulate the data. It makes it easy to work with tabular data and is the default data tool for most data-science work.\n",
    "\n",
    "The dataformat is a [DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "BXLCqnWo_X46"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('adult.data', names=column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
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    "colab_type": "code",
    "id": "X7DMI-Vb_inw",
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   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>WorkClass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationNum</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Relationship</th>\n",
       "      <th>Race</th>\n",
       "      <th>Gender</th>\n",
       "      <th>CapitalGain</th>\n",
       "      <th>CapitalLoss</th>\n",
       "      <th>HoursPerWeek</th>\n",
       "      <th>NativeCountry</th>\n",
       "      <th>Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age          WorkClass  fnlwgt   Education  EducationNum  \\\n",
       "0   39          State-gov   77516   Bachelors            13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors            13   \n",
       "2   38            Private  215646     HS-grad             9   \n",
       "3   53            Private  234721        11th             7   \n",
       "4   28            Private  338409   Bachelors            13   \n",
       "\n",
       "         MaritalStatus          Occupation    Relationship    Race   Gender  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   CapitalGain  CapitalLoss  HoursPerWeek   NativeCountry  Income  \n",
       "0         2174            0            40   United-States   <=50K  \n",
       "1            0            0            13   United-States   <=50K  \n",
       "2            0            0            40   United-States   <=50K  \n",
       "3            0            0            40   United-States   <=50K  \n",
       "4            0            0            40            Cuba   <=50K  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# quick look at the top few rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "xYhFBrH2_n5w",
    "outputId": "5d0945fc-b911-427c-f366-fa5c9438dce7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32561, 15)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and get a sense of the size of the dataframe\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7Luvc3wnXiX-"
   },
   "source": [
    "Pandas can access columns by their string names. In this case we want to turn the `Income` column into a target variable (the thing we are trying to predict). Everything matching ` >50K` is turned into a `True` and everything else is a `False`. This 'binary target' is the bread-and-butter of classification.\n",
    "\n",
    "As a technical note, when you use a string to grab a column from a pandas dataframe, it returns a [Series](https://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.Series.html). I use the `.values` attribute to get a numpy array and then turn the `True` and `False` values into `1` and `0` values by [casting](https://www.w3schools.com/python/python_casting.asp) the values to `int`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "CO9RcSqi_rbA"
   },
   "outputs": [],
   "source": [
    "y = (df['Income'] == ' >50K').values.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "tDkZt0pzAmdB"
   },
   "outputs": [],
   "source": [
    "use_columns = ['Age', 'HoursPerWeek']\n",
    "X = df[use_columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hHdcfe8XU4dT"
   },
   "source": [
    "We are almost ready to fit our first model, but before we do, we need to create training and testing sets. Why is it necessary to split our data? We want to be sure we are not [overfitting](https://elitedatascience.com/overfitting-in-machine-learning) ([wiki](https://en.wikipedia.org/wiki/Overfitting)).\n",
    "\n",
    "If we had a model that fit the training perfectly, it may not generalize well to new data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "4HttoqzaBicb"
   },
   "outputs": [],
   "source": [
    "# splits both X and y into train and testing data - I am using the `random_state` argument to reproduce the same results\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=432345)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "o_jyGAq8UMHq"
   },
   "source": [
    "## Logistic Regression\n",
    "\n",
    "Let's start with one of the simplest models: [logistic regression](https://en.wikipedia.org/wiki/Logistic_regression). By default, this model adds \"[regularization](https://developers.google.com/machine-learning/crash-course/regularization-for-simplicity/l2-regularization)\". In this case an \"L2\" regularization. In practical terms this makes a model that 1) tries to fit the data as well as possible while 2) keep the coefficients as small as possible. This second part helps to avoid overfitting by not letting the model fit too closely to our training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "MeGHUirqBi9g"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "RZrAj0ApIp_V"
   },
   "outputs": [],
   "source": [
    "# initialize the model by calling the class `LogisticRegression`\n",
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 139
    },
    "colab_type": "code",
    "id": "Dq6CTzYqIqvk",
    "outputId": "e39d67ce-60c2-42f6-c430-c109764168cb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now that we have our model we can call the `fit` method\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "yxDtU321JAUW",
    "outputId": "15e83405-1138-40b9-a2fe-aa32f6cb61e4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the predicted classes (0 or 1)\n",
    "model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 136
    },
    "colab_type": "code",
    "id": "dYs1sYAkJpvT",
    "outputId": "35a037b9-093c-4ef9-8dc2-f08bb35fc5b5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.6119385 , 0.3880615 ],\n",
       "       [0.51311941, 0.48688059],\n",
       "       [0.91545097, 0.08454903],\n",
       "       ...,\n",
       "       [0.93442776, 0.06557224],\n",
       "       [0.84230077, 0.15769923],\n",
       "       [0.81645598, 0.18354402]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the odds (probability) of each class this is an Nx2 array where the first column contains the probability of the first class\n",
    "model.predict_proba(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "y9C6yKoBRBI7"
   },
   "source": [
    "### Testing our model\n",
    "\n",
    "We want to see how good our model is at fitting the data. This is a classification model, so one sensible way of seeing how good our model would be would be to look at the \"accuracy\", that is, how often does something that we say should happen actually happen and how often does something we say shouldn't happen not happen (see: https://en.wikipedia.org/wiki/Evaluation_of_binary_classifiers for more). \n",
    "\n",
    "Accuracy can be a misleading metric based on the base rate of the event. If the event usually happens 99% of the time, a classifier could get 99% accuracy by just guessing that event all the time. \n",
    "\n",
    "A better metric would balance both the true positive rate (how often the thing we predicted happened) and the false positive rate (how often the thing we predicted didn't happen). A popular metric for that is the \"area under the receiving operator curve\" or [AUROC](http://fastml.com/what-you-wanted-to-know-about-auc/) (sometimes just called AUC).\n",
    "\n",
    "This metric goes from 0.5 to 1.0, where 0.5 is a random classifier and 1.0 is a perfect classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "UlC1F8-hJr2c"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "BiZPOS_vKtEE"
   },
   "outputs": [],
   "source": [
    "# we are going to get the false positive rates and true positive rates to plot\n",
    "fpr, tpr, thresholds = roc_curve(y_test, model.predict_proba(X_test)[:, 1])\n",
    "# and the total area under the curve\n",
    "auroc = roc_auc_score(y_test, model.predict_proba(X_test)[:, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "LD1udTjoK7L5"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "colab_type": "code",
    "id": "ugrk5logLGJf",
    "outputId": "e4a64adf-0c36-4bc2-ee4f-28060cfbd6be"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'AUROC: 0.73')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CbQiszzh48CAdO3bk9ttvZ9myZTRp0sTbIZkAkVlRwJ9uct8ZfYKknyUpBKgE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\nKABYAWKnQ5+JuLpzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the ROC\n",
    "fig, axis = plt.subplots()\n",
    "axis.plot(fpr, tpr)\n",
    "axis.set_xlabel('False Positive Rate')\n",
    "axis.set_ylabel('True Positive Rate')\n",
    "\n",
    "axis.plot([0, 1], [0, 1], color='black', linestyle='--')\n",
    "axis.set_xlim([0, 1])\n",
    "axis.set_ylim([0, 1])\n",
    "axis.set_title(f'AUROC: {auroc:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ay_hpG2fXHBr"
   },
   "source": [
    "We can look at the coefficients of our model by examining the `coef_` attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "9lmVVkA-NgAc",
    "outputId": "78c94cdc-a3e9-470d-96f6-4783da6ff6a7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.04314066, 0.04641807]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mRL1Mhc9R97B"
   },
   "source": [
    "Since we will need to make this plot often, let's make it into a function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "f_Tl5FsvM-C_"
   },
   "outputs": [],
   "source": [
    "def make_auc_plot(y_true: np.ndarray, y_pred: np.ndarray) -> None:\n",
    "    \"\"\"\n",
    "    Make a beautiful plot of the receiver operating characteristic curve\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    y_true: ndarray\n",
    "        An array of the true labels (1s for True, 0s for False)\n",
    "    y_pred: ndarray\n",
    "        An array of the predictions (between 0 and 1)\n",
    "    \n",
    "    \n",
    "    Side Effects\n",
    "    ------------\n",
    "    Produces a matplotlib figure\n",
    "    \"\"\"\n",
    "  \n",
    "    fpr, tpr, thresholds = roc_curve(y_true, y_pred)\n",
    "    auroc = roc_auc_score(y_true, y_pred)\n",
    "  \n",
    "    fig, axis = plt.subplots()\n",
    "    axis.plot(fpr, tpr)\n",
    "    axis.set_xlabel('False Positive Rate')\n",
    "    axis.set_ylabel('True Positive Rate')\n",
    "\n",
    "    axis.plot([0, 1], [0, 1], color='black', linestyle='--')\n",
    "    axis.set_xlim([0, 1])\n",
    "    axis.set_ylim([0, 1])\n",
    "    axis.set_title(f'AUROC: {auroc:.2f}')  # using formating codes here https://pyformat.info/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# New model types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6YaVMHd7N5jy"
   },
   "source": [
    "## Decision Trees\n",
    "\n",
    "These flexible models can fit complicated functions with interactions between features and ability to handle different data. They tend to not fit that well and to overfit.\n",
    "[Wiki](https://en.wikipedia.org/wiki/Decision_tree) [SKLearn Docs](https://scikit-learn.org/stable/modules/tree.html)\n",
    "\n",
    "An example of a decision tree:\n",
    "\n",
    "![Example Tree](https://scikit-learn.org/stable/_images/sphx_glr_plot_iris_dtc_0021.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "RRW_ftn2LnXU"
   },
   "outputs": [],
   "source": [
    "# again we will import a new model type from sklearn\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "GlrLjuRyMvwV"
   },
   "outputs": [],
   "source": [
    "# we initialize the model using the same structure as before\n",
    "tree_model = DecisionTreeClassifier(max_depth=10, min_samples_leaf=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "2f_aCPBrMw3d",
    "outputId": "07512a42-e9bb-4764-c31f-6a2bb1da1a6d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=10,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=10, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "oLDfg94FNPsO",
    "outputId": "5056ab28-9d7f-4ddf-d970-fd28e3e3ce7c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0ISwszOmwTADx5IG7qxUHVMgwXR5Xh3jmdWaraoqq7gV24EocJpepKtOW72XI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\nYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = tree_model.predict_proba(X_test)[:, 1]\n",
    "make_auc_plot(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "2DOO0nGaNVqF",
    "outputId": "a69c9adf-1158-493d-89c9-48bf496800b7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ejCmguW6BdU049HYvm0siUKkqU6dOpVq1asyePZuxY8daET/jVR7XbRCRXKp6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\nhnDHHXcwZcoUK+JnTIDJ7Iq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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = tree_model.predict_proba(X_train)[:, 1]\n",
    "make_auc_plot(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lM_sRWErQ3Qa"
   },
   "source": [
    "## Add Another Feature\n",
    "\n",
    "Let's add a new feature `MaritalStatus` to our model. We run into a problem in that Marital Status is a feature of different strings. We need to find a way to 'encode' this value into a numeric value.\n",
    "\n",
    "The method we will use here is called 'dummy encoding' or 'one-hot' encoding. We will create a new feature for each string in the old feature and then give that row a 1 for the new feature if it takes that value (e.g. we would create a 'Divorced' feature and then place a 1 in each row that had an `MaritalStatus` value 'Divorced'). Bonus: there are some 'fancier' ways that one could use to encode categorical variables for instance in this [blog post](https://towardsdatascience.com/smarter-ways-to-encode-categorical-data-for-machine-learning-part-1-of-3-6dca2f71b159)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "xEUfQVpqR14t"
   },
   "outputs": [],
   "source": [
    "# We could also do this directly in sklearn:\n",
    "#https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html\n",
    "marital_status_dummies = pd.get_dummies(df['MaritalStatus'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "436oEshZR8xK",
    "outputId": "2539a202-2907-4af4-a809-537d95c7e631"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Divorced</th>\n",
       "      <th>Married-AF-spouse</th>\n",
       "      <th>Married-civ-spouse</th>\n",
       "      <th>Married-spouse-absent</th>\n",
       "      <th>Never-married</th>\n",
       "      <th>Separated</th>\n",
       "      <th>Widowed</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Divorced   Married-AF-spouse   Married-civ-spouse   Married-spouse-absent  \\\n",
       "0          0                   0                    0                       0   \n",
       "1          0                   0                    1                       0   \n",
       "2          1                   0                    0                       0   \n",
       "3          0                   0                    1                       0   \n",
       "4          0                   0                    1                       0   \n",
       "\n",
       "    Never-married   Separated   Widowed  \n",
       "0               1           0         0  \n",
       "1               0           0         0  \n",
       "2               0           0         0  \n",
       "3               0           0         0  \n",
       "4               0           0         0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marital_status_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "nmyRs9p7R-XV"
   },
   "outputs": [],
   "source": [
    "use_columns = ['Age', 'HoursPerWeek', 'MaritalStatus']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = pd.get_dummies(df[use_columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "p3Th44huSRQa",
    "outputId": "50aa1f37-fe7a-41cc-f032-a7f18c065549"
   },
   "outputs": [
    {
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       "      <th>MaritalStatus_ Married-civ-spouse</th>\n",
       "      <th>MaritalStatus_ Married-spouse-absent</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  HoursPerWeek  MaritalStatus_ Divorced  \\\n",
       "0   39            40                        0   \n",
       "1   50            13                        0   \n",
       "2   38            40                        1   \n",
       "3   53            40                        0   \n",
       "4   28            40                        0   \n",
       "\n",
       "   MaritalStatus_ Married-AF-spouse  MaritalStatus_ Married-civ-spouse  \\\n",
       "0                                 0                                  0   \n",
       "1                                 0                                  1   \n",
       "2                                 0                                  0   \n",
       "3                                 0                                  1   \n",
       "4                                 0                                  1   \n",
       "\n",
       "   MaritalStatus_ Married-spouse-absent  MaritalStatus_ Never-married  \\\n",
       "0                                     0                             1   \n",
       "1                                     0                             0   \n",
       "2                                     0                             0   \n",
       "3                                     0                             0   \n",
       "4                                     0                             0   \n",
       "\n",
       "   MaritalStatus_ Separated  MaritalStatus_ Widowed  \n",
       "0                         0                       0  \n",
       "1                         0                       0  \n",
       "2                         0                       0  \n",
       "3                         0                       0  \n",
       "4                         0                       0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "-ijIYpeYSVAO"
   },
   "outputs": [],
   "source": [
    "# now that we have new columns we need to redo our train/test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=3523523)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 139
    },
    "colab_type": "code",
    "id": "hr_kKpiiSdJg",
    "outputId": "283e64fa-d643-4a4c-c7b1-cb0ce0859736"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "yytE2TMTSi-1",
    "outputId": "48e30697-8457-4bb8-b61a-e7933735af5f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Txr/t27eP+++/n0GDBjF06FDGjBnjdEg+Z+UxjSNW772cJLqElePX8e0tSRi/5nK5ePfd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\n/wOtoH0iBlwKkAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model.predict_proba(X_test)[:, 1]\n",
    "make_auc_plot(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-GnF2JhzT0Q-"
   },
   "source": [
    "Sometimes there are lots of categories. For instance the `NativeCountry` feature has 42 unique values. We want to add this information to our model, but adding 42 features seems like a lot for our simple model. Let's add just an 'indicator' if the person came from the USA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "3MMrx2UhTQT6",
    "outputId": "f5453484-438f-4f65-d3d9-985a42891666"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.unique(df['NativeCountry']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "PJIhf1peVkd2"
   },
   "outputs": [],
   "source": [
    "# we can add a new column like we would add an entry to a dictionary\n",
    "X['from_USA'] = (df['NativeCountry'] == ' United-States').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "C8t-vbWXVxh6"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=4535234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 139
    },
    "colab_type": "code",
    "id": "qKNGwGRmWBmq",
    "outputId": "9d407d7b-ed00-4293-979a-52fbc9db8081"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "APviKyfOWDPI",
    "outputId": "8650a708-f169-4343-bf3b-5dbb1dcb86e0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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q7gK24k4cBnj5p80AjG5np8T81aFDh7j77ruJjIy8WMTv1ltvtSRhMp0vE8UK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\nk9sUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model.predict_proba(X_test)[:, 1]\n",
    "make_auc_plot(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "fEhami9jW9dY",
    "outputId": "228364b4-2f5d-4266-9177-932487762d62"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=10,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=10, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "ljEdO5U9XBoe",
    "outputId": "d95add84-8c15-460e-ce8e-506722cc44b9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FAxlrX1fB3SGeeZ1vVDVVVXcB23Anjnzpv8v3UKVUIQZHWslwc3kHDx7ktttu\nIzIy8nwRv549e1qSMLnOl4liFVBXRGqKSAFgKDA30zpfA90APM9qXAvE+jAmv/Xrtj/4bUcC/ZtU\nIjzUl/8tJtCpKu+99x4NGzbk22+/5dlnn7UifsanvKn1BICIFFTVs96ur6ppInIvMB93/8NsVY0W\nkaeB1ao617Osl4hsBtKBR1T1yJX9CIFv79Ez3PPhWmqXK8Lo9jWcDsf4uaFDh/Lpp5/SoUMHZs6c\nSf369Z0OyQQ5Uc2+yV9EWgOzgBKqWk1EmgETVPUveRFgZpGRkbp69WonvtonXC5l2IzlRO8/wQ9/\n7USVUoWdDsn4oYxF/N577z1OnjzJ3XffTUiIXX0a74jIGlWNzMlnvfktew0YABwBUNUNeJqLzNV7\n5/fdrNh1lL8PaGhJwmRp69atdO7cmVmzZgEwZswY7r33XksSJs9485sWoqp7Ms1L90Uw+U3MHyd5\n8YetdK9fntsirc6iuVhqairPPvsszZo1Y/PmzRQtWtTpkEw+5U0fxV5P85N6nrb+C2BDoeaCf367\nmUIFQnluUBMbpc5cZP369YwbN47169dz66238vrrr1OxYkWnwzL5lDeJ4i7czU/VgEPAT5555ips\njE/ktx0JTO5Tn/LF7HZGc7GDBw9y8OBBvvjiC2655XJFEIzxLW8SRZqqDvV5JPlIWrqLF+dvpVjB\nMEa0Dd4nzc2VWbJkCVFRUdx999306dOHnTt3Uriw9VsZ53nTR7FKROaJyBgRKebziPKBlxZs57cd\nCTzapx7FI8KdDsc47OTJk9x777106tSJV1555XwRP0sSxl9cNlGoam3gGaAVsFFEvhYRu8LIoc37\nTzB98U6GRFZlVLsaTodjHDZ//nwaN27MW2+9xf3338/atWutiJ/xO17dX6eqv6vqfUBL4ATuAY1M\nDkxbvJPCBcL4W78GTodiHLZ3714GDBhA4cKFWbJkCa+88ord2WT80mUThYgUFZERIvItsBI4DFi9\ngBw4mJjM/6IOMDiyKiUKW5NTfqSqrFy5EoCqVavy/fffs27dOivBYfyaN1cUm4C2wIuqWkdVH1LV\nFT6OKyi9t2w3LlXGdajhdCjGAQcOHGDQoEG0adPmfBG/Hj16WBE/4/e8ueuplqq6fB5JkDuTksZH\nK+Lo3agiVUtbJ2V+oqq8++67PPjggyQnJ/PCCy/QoUMHp8MyxmuXTBQi8rKqPgR8ISJ/KgjlxQh3\nJoMv1sSTmJTK7R1rOh2KyWODBw/m888/p1OnTsycOZNrr73W6ZCMuSLZXVF84vn3Ske2M5m4XMrs\npbtpVqUEraqXcjockwfS09MKYzMgAAAekElEQVQREUJCQrjhhhu4/vrrueOOO6w+kwlIl/ytVdWV\nnrcNVPXnjC/Abtm5Aou2H2ZXwmnGd6xppTrygS1bttCpU6fzRfxGjx7NXXfdZUnCBCxvfnPHZzHv\n9twOJJi9v2w35YoVpG/jSk6HYnwoNTWVZ555hubNm7Nt2zZKlCjhdEjG5Irs+iiG4B6VrqaIfJlh\nUTHgeNafMpntOXKaX7cf5i/X16VAmJ1RBqt169YxduxYoqKiGDJkCK+99hrly5d3OixjckV2fRQr\ncY9BUQV4M8P8k8A6XwYVTP67fA+hIoxoYzWdgtmhQ4dISEjg66+/ZuDAgU6HY0yuumSiUNVdwC7c\n1WJNDiSlpPPp6nh6N65IheJ2r3ywWbx4MRs3buSee+6hT58+xMTEUKhQIafDMibXXbItREQWef49\nJiJHM7yOicjRvAsxcM3dsI/EpFRGt63udCgmF504cYK7776bLl268Nprr50v4mdJwgSr7BrNzw13\nWhYol+F1btpkIzXdxewlu6lfsRita5Z2OhyTS+bNm0ejRo2YNm0aDz74oBXxM/lCdrfHnnsauyoQ\nqqrpQDvgDqBIHsQW0F79aQfbDp3krz3q2i2xQWLv3r0MHDiQEiVK8Pvvv/Pyyy9TpIj9KZjg581t\nOF/jHga1NvA+7mcoPvJpVAFuRewR3vw1hsGRVehjt8QGNFVl+fLlgLuI34IFC1i7di1t2rRxODJj\n8o43icKlqqnALcArqvoXoLJvwwpcR06d5YFP1lOjTBGeuqGR0+GYq7B//35uuukm2rVrd76IX7du\n3ShQoIDDkRmTt7xJFGkichswCvjOM89qZGchJc3Fnf9dw5HTKbw+rAVFCnpTc9H4G1Vl5syZNGzY\nkAULFvDSSy9ZET+Tr3lzJBsP3I27zHisiNQEPvZtWIFHVXny642s2n2M14e1oHFleyo3UN166618\n+eWXdOnShZkzZ1KnTh2nQzLGUZdNFKq6SUTuA+qISH0gRlX/5fvQAsvspbv5dHU8911fhxuaXeN0\nOOYKZSzid9NNN9GrVy8mTpxo9ZmMwbsR7joBMcAsYDawXUTsOjyDxdsP86//baZ3owr8tYeVkA40\nmzZtokOHDueL+I0aNcoqvRqTgTd/Cf8B+qlqB1VtD/QHXvVtWIEj3aU8NTeaWuWKMmVwc0JC7FbY\nQJGSksI///lPWrZsyc6dOylVykrAG5MVb/ooCqjq5nMTqrpFROy2D4/vNx1gV8Jp3hrR0jqvA8ia\nNWsYO3YsmzZtYvjw4bzyyiuUK2fPkRqTFW+ObGtFZBrwgWd6BFYUEHB3YL+1cCe1yhWhd6OKTodj\nrsCRI0c4fvw43377LQMGDHA6HGP8mjeJ4k7gPuBRQIDFwOu+DCpQfL/pIJsPnOCl25oRak1Ofm/h\nwoVs3LiR++67j169erFjxw4iIqxYozGXk20fhYg0AfoAX6nqjap6g6r+W1WT8yY8//a/qANULB7B\nzS3s+UN/lpiYyB133MH111/P22+/fb6InyUJY7yTXfXYv+Eu3zEC+FFEshrpLt9yuZTfdybQoU5Z\nu5rwY99++y0NGzZk5syZPPzww6xZs8aK+BlzhbJrehoBNFXV0yJSDpiH+/ZYA2w+cIJjZ1LpWLeM\n06GYS9i7dy+DBg2ifv36fP3111x33XVOh2RMQMqu6emsqp4GUNXDl1k331kakwBA+9plHY7EZKSq\n/P7778CFIn6rV6+2JGHMVcju4F9LRL70vL4CameY/jKbz50nIn1EZJuIxIjIY9msd6uIqIhEXukP\n4JSlO49Qt3xRG7nOj8THx3PjjTfSoUOH80X8unbtakX8jLlK2TU9Dco0/caVbFhEQnGPtd0TiAdW\nicjcjM9keNYrhvuuqhVXsn0nnU1LZ+WuIwy9zsbB9gcul4sZM2bwyCOPkJaWxpQpU+jYsaPTYRkT\nNLIbM/vnq9x2a9x1oWIBRGQOMBDYnGm9/wNeBB6+yu/LM79uO0xyqosOdazZyR8MGjSIr7/+muuv\nv54ZM2ZQq1Ytp0MyJqj4st+hMrA3w3Q8mcaxEJEWQFVV/Y5siMgkEVktIqsPHz6c+5FegXSX8vKC\nbdQsW4Su9exJXqekpaXhcrkHYRw0aBAzZszgp59+siRhjA/4MlFkdc+onl8oEoK7jtRDl9uQqk5X\n1UhVjXS6zMKXa+PZfugUD/eqR3io9e87ISoqinbt2jFjxgwARo4cyYQJE2zIWWN8xOsjnYhc6c3n\n8bjH2z6nCrA/w3QxoDHwq4jsBtoCc/25Qzs5NZ3//LidplVK0K+JlezIa2fPnuWpp56iVatW7Nmz\nx2ozGZNHvCkz3lpENgI7PNPNRMSbEh6rgLoiUtNTRHAoMPfcQlVNVNWyqlpDVWsAy4EbVXV1Tn6Q\nvPDf5XvYn5jMY33q29lrHlu1ahUtW7bk6aefZtiwYWzZsoVbbrnF6bCMyRe8qfX0GjAA91PaqOoG\nEel2uQ+papqI3AvMB0KB2aoaLSJPA6tVdW72W/AvJ5JTeWNhDJ3qlqW9dWLnuWPHjnHq1CnmzZtH\n3759nQ7HmHzFm0QRoqp7Mp1Bp3uzcVWdh/uJ7ozz/n6Jdbt6s02nTFu0k+NnUpncp77ToeQbv/zy\nCxs3buT++++nV69ebN++3cpvGOMAb/oo9opIa0BFJFRE/gps93FcfuXQiWRmLdnFjc2usbGw88Dx\n48eZOHEi3bt3Z9q0aeeL+FmSMMYZ3iSKu4AHgWrAIdydznf5Mih/8+rPO0h3KQ/3qud0KEHvm2++\noWHDhsyePZtHH33UivgZ4wcu2/Skqn/g7ojOl3YePsUnq/Yysk01qpUp7HQ4QS0uLo7bbruNBg0a\nMHfuXCIj/fYGOGPylcsmChGZQYbnH85R1Uk+icjPvLxgGxFhIfyle12nQwlKqsqSJUvo1KkT1apV\n46effqJt27ZWn8kYP+JN09NPwM+e11KgPHDWl0H5i/V7jzNv40EmdKpF2aLW/JHb4uLi6N+/P507\ndz5fxK9z586WJIzxM940PX2ScVpEPgB+9FlEfkJVeeH7rZQpUoCJna0sRG5yuVxMnTqVyZMno6q8\n9tprVsTPGD/mze2xmdUEqud2IP5m8Y4ElsUe4R83NKRowZzsJnMpt9xyC9988w09e/Zk+vTp1KhR\nw+mQjDHZ8KaP4hgX+ihCgKPAJceWCAYul/L891upWroQw9sEfU7ME2lpaYSEhBASEsKQIUMYOHAg\nY8eOtSfcjQkA2fZRiPuvuBlQzvMqpaq1VPXTvAjOKd9G7WfLgRM83KseBcKs8N/V2rBhA23atGH6\n9OkADBs2jHHjxlmSMCZAZHsUVFUFvlLVdM/rT3c/BZuUNBcvLdhGw0rFuaHpNU6HE9CSk5N58skn\niYyMJD4+nooVrZCiMYHIm8b3lSLSUlXX+jwaP/DRij3sPZrEe+ObEBJiZ7w5tXLlSsaMGcPWrVsZ\nM2YMU6ZMoXTp0k6HZYzJgUsmChEJU9U0oCMwUUR2AqdxjzOhqtoyj2LMM6fOpvH6LzG0q1WGznWt\n8N/VOHHiBElJSfzwww/07t3b6XCMMVchuyuKlUBL4KY8isVxMxbHcuR0Co/1tTLiObFgwQKio6N5\n4IEH6NGjB9u2bbPyG8YEgez6KARAVXdm9cqj+PLM4ZNnmfFbLP2aVKRZ1ZJOhxNQjh07xrhx4+jd\nuzezZs2yIn7GBJnsrijKiciDl1qoqlN8EI9j3vhlB2fTXFb47wp9+eWX3HPPPRw+fJjHH3+cv//9\n75YgjAky2SWKUKAoWY99HVT2HDnNRyvjGHJdVWqVK+p0OAEjLi6OoUOH0rhxY+bNm0eLFi2cDskY\n4wPZJYoDqvp0nkXioJcXbCcsJIS/WuG/y1JVFi9eTJcuXahWrRq//PILbdq0ITw83OnQjDE+ctk+\nimC3aV8iczfsZ3zHGpQvHuF0OH5tz5499O3bl65du54v4texY0dLEsYEuewSRfc8i8JBL/ywlZKF\nw7mjS22nQ/FbLpeLN954g0aNGrFkyRJef/11OnXq5HRYxpg8csmmJ1U9mpeBOGFpTAK/7Ujgyf4N\nKB5hZ8WXctNNN/Htt9/Su3dvpk2bRvXqVv/KmPwk35ZFVVVe+GEr15SIYGRbO/BllpqaSmhoKCEh\nIQwbNoxbb72VUaNG2fMlxuRD+bbi3bdRB4iKT+TBXvWICA91Ohy/snbtWlq3bs3UqVMBdxG/0aNH\nW5IwJp/Kl4liaUwC989ZR70Kxbi5RWWnw/EbSUlJPP7447Ru3ZqDBw9StWpVp0MyxviBfNn09NGK\nOEoVLsCssZGEWuE/AJYvX86YMWPYvn0748eP56WXXqJUqVJOh2WM8QP5LlGoKit2HaHLteWoUqqw\n0+H4jdOnT5OamsqPP/5Ijx49nA7HGONH8l2iiE04TcKpFNrUtJLXP/zwA9HR0Tz00EN0796drVu3\nUqBAAafDMsb4mXzXR7Ei1n3Xb+t8nCiOHDnCmDFj6Nu3L++99x4pKSkAliSMMVnKd4li5a4jlC1a\nkJplizgdSp5TVT7//HMaNmzIRx99xJNPPsmqVassQRhjspWvmp7c/RNHaVOrdL681TMuLo7hw4fT\ntGlTFixYQLNmzZwOyRgTAPLVFUX8sSQOJCbnq/4JVeWXX34BoHr16vz6668sX77ckoQxxmv5KlEs\njUkAoE3NMg5Hkjd27dpFr1696N69+/kifu3btycsLF9dSBpjrlK+SRT7jifx7/nbqFu+KHXLB/eY\nE+np6bz66qs0btyYFStW8Pbbb1sRP2NMjuWLU8vk1HTu+u8azqa5mDqqFSFB/pDdwIED+d///ke/\nfv2YOnWqPWFtjLkq+SJRvLN0N1HxiUwf1YraQTqCXcYifqNGjWLYsGEMHz48X3baG2Nyl0+bnkSk\nj4hsE5EYEXksi+UPishmEYkSkZ9FxCdlXJfHHqFehWL0alTRF5t33OrVq4mMjOTtt98GYMiQIYwY\nMcKShDEmV/gsUYhIKPAm0BdoCAwTkYaZVlsHRKpqU+Bz4MXcjuPrdftYtP0wLauXzO1NOy4pKYnJ\nkyfTpk0bDh8+bONEGGN8wpdXFK2BGFWNVdUUYA4wMOMKqrpQVc94JpcDVXI7iJlLYilcIJTbO9bM\n7U07atmyZTRr1owXX3yR8ePHs3nzZgYMGOB0WMaYIOTLPorKwN4M0/FAm2zWvx34PqsFIjIJmARQ\nrVo1rwPYdzyJTftO8Fjf+tQpX8zrzwWCpKQkXC4XP/30E92754tRa40xDvFlosiqgVyzXFFkJBAJ\ndMlquapOB6YDREZGZrmNrPwYfRCAXg0rePsRvzZv3jyio6N55JFHuP7669myZQvh4TaEqzHGt3zZ\n9BQPZLwvswqwP/NKItIDeAK4UVXP5mYAP245RJ3yRakV4Hc6JSQkMHLkSPr378+HH354voifJQlj\nTF7wZaJYBdQVkZoiUgAYCszNuIKItACm4U4Sf+TmlyeeSWV57NGAvppQVebMmUODBg349NNPeeqp\np1i5cqUV8TPG5CmfNT2papqI3AvMB0KB2aoaLSJPA6tVdS7wb6Ao8JnnVs44Vb0xN77/l22HSHcp\nPQM4UcTFxTFmzBiaNWvGrFmzaNKkidMhGWPyIZ8+cKeq84B5meb9PcN7nw2ltiD6EOWLFaRZlcC6\nLVZV+fnnn+nRowfVq1dn0aJFXHfddYSGhjodmjEmnwrKWk/Jqeks2n6Yng0rBFS5jp07d9K9e3d6\n9ux5vohf27ZtLUkYYxwVlIni950JnElJD5gnsdPT05kyZQpNmjRhzZo1TJs2zYr4GWP8RlDWeloQ\nfYhiBcNoVyswyonfcMMNfP/99wwYMIC3336bKlVy/blDY4zJsaBLFOku5acth+hSrxwFwvz3gikl\nJYWwsDBCQkIYO3Yso0aNYujQoVafyRjjd/z3SJpD6+KOkXAqxa+bnVauXEmrVq146623ABg8eDDD\nhg2zJGGM8UtBlyh+3HyI8FCha71yTofyJ2fOnOGhhx6iXbt2HDt2jNq1azsdkjHGXFZQNT2pKvOj\nD9KudlmKR/jXU8tLlixhzJgxxMbGcscdd/DCCy9QokQJp8MyxpjLCqpEEfPHKXYfOcOETrWcDuVP\nzg0stHDhQrp27ep0OMYY47WgShQLNh8C8Junsb/99lu2bNnCo48+Srdu3di8eTNhYUG1y40x+UBQ\n9VEs2HyIZlVLUqF4hKNxHD58mOHDh3PjjTfy8ccfny/iZ0nCGBOIgiZRHExMZsPe444WAVRVPvro\nIxo0aMDnn3/O008/zYoVK6yInzEmoAXNKe7Cbe7is042O8XFxTFu3DhatGjBrFmzaNSokWOxGGNM\nbgmaK4olMQlULB5B3fJ5O/aEy+Vi/vz5AFSvXp3ffvuNpUuXWpIwxgSNoEgULpfye0wC7euUydOH\n1nbs2MH1119Pnz59WLx4MQCtW7e2In7GmKASFIliy8ETHDuTSsc6ZfPk+9LS0vj3v/9N06ZNWb9+\nPbNmzbIifsaYoBUUfRRLYxIA6JBHiWLAgAHMnz+fgQMH8tZbb3HNNdfkyfcaY4wTgiJRLIk5Qt3y\nRX16W+zZs2cJDw8nJCSECRMmMH78eG677Tarz2SMCXoB3/R0Ni2dlbuO+PRqYvny5bRs2ZI333wT\ngFtvvZXBgwdbkjDG5AsBnyjWxR0nOdXlk0Rx+vRpHnjgAdq3b8/JkyepW7durn+HMcb4u4Bveloa\nk0BoiNCmVulc3e5vv/3GmDFj2LVrF3fffTfPPfccxYsXz9XvMMaYQBDwiWJJTAJNq5TI9WqxaWlp\nhIeHs2jRIjp37pyr2zbGmEAS0E1PJ5JTiYpPzLXbYr/++muee+45ALp160Z0dLQlCWNMvhfQiWJF\n7FHSXXrV/ROHDh1i8ODB3HzzzXz++edWxM8YYzII6ESxNCaBQuGhtKhWMkefV1U++OADGjZsyDff\nfMO//vUvli9fbkX8jDEmg4A9ZT6TksaXa+O5rmZpCoblrGRGXFwcEyZMIDIyklmzZlG/fv1cjtIY\nYwJfwF5RvPrzDk4kp9G9fvkr+pzL5eL7778H3EX8li5dyuLFiy1JGGPMJQRsoli9+xh1yxdldLvq\nXn9m+/btdO3alX79+rFo0SIAIiMjrYifMcZkIyATRUqai437Eular5xXT0enpaXxwgsv0LRpUzZu\n3Mg777xjdzMZY4yXArKPYvOBE6SkuWhRrZRX6/fv358FCxZwyy238Oabb1KxYkUfR2iMMcEjIBPF\nurhjANne7ZScnEx4eDihoaFMmjSJSZMmMWjQoLwK0RhjgkZANj2tjTtOpRIRVCpRKMvlS5cupXnz\n5ueL+A0aNMiShDHG5FBAJop1cceyvJo4deoU9913H506dSI5OZkGDRo4EJ0xxgSXgEsUaS4l/lgS\nLape3D+xaNEiGjduzBtvvMG9997Lpk2b6Nmzp0NRGmNM8Ai4PoqklDQAmmdxRVG4cGF+++03OnTo\nkNdhGWNM0Aq4RHE6JZ0woFbZInz55Zds3bqVv/3tb3Tp0oWNGzfaMxHGGJPLfNr0JCJ9RGSbiMSI\nyGNZLC8oIp94lq8QkRqX2+ax0ym0qxTCHWNHMGjQIL766qvzRfwsSRhjTO4TVfXNhkVCge1ATyAe\nWAUMU9XNGda5G2iqqneKyFDgZlUdkt12w0pU0EKcJfVsMv/4xz946KGHCA/P3bEojDEm2IjIGlWN\nzMlnfXlF0RqIUdVYVU0B5gADM60zEHjP8/5zoLtc5lHr9BOHad60CRs2bOCxxx6zJGGMMT7myz6K\nysDeDNPxQJtLraOqaSKSCJQBEjKuJCKTgEmeybNLlizZZEX8AChLpn2Vj9m+uMD2xQW2Ly6ol9MP\n+jJRZHVlkLmdy5t1UNXpwHQAEVmd08unYGP74gLbFxfYvrjA9sUFIrI6p5/1ZdNTPFA1w3QVYP+l\n1hGRMKAEcNSHMRljjLlCvkwUq4C6IlJTRAoAQ4G5mdaZC4zxvL8V+EV91btujDEmR3zW9OTpc7gX\nmA+EArNVNVpEngZWq+pcYBbwgYjE4L6SGOrFpqf7KuYAZPviAtsXF9i+uMD2xQU53hc+uz3WGGNM\ncAi4Wk/GGGPyliUKY4wx2fLbROGL8h+Byot98aCIbBaRKBH5WUS8H0g8wFxuX2RY71YRUREJ2lsj\nvdkXIjLY87sRLSIf5XWMecWLv5FqIrJQRNZ5/k76ORGnr4nIbBH5Q0Q2XWK5iMhrnv0UJSItvdqw\nqvrdC3fn906gFlAA2AA0zLTO3cBUz/uhwCdOx+3gvugGFPa8vys/7wvPesWAxcByINLpuB38vagL\nrANKeabLOx23g/tiOnCX531DYLfTcftoX3QGWgKbLrG8H/A97mfY2gIrvNmuv15R+KT8R4C67L5Q\n1YWqesYzuRz3MyvByJvfC4D/A14EkvMyuDzmzb6YCLypqscAVPWPPI4xr3izLxQo7nlfgj8/0xUU\nVHUx2T+LNhB4X92WAyVFpNLltuuviSKr8h+VL7WOqqYB58p/BBtv9kVGt+M+YwhGl90XItICqKqq\n3+VlYA7w5vfiWuBaEVkqIstFpE+eRZe3vNkX/wBGikg8MA/4S96E5neu9HgC+O94FLlW/iMIeP1z\nishIIBLo4tOInJPtvhCREOA/wNi8CshB3vxehOFufuqK+yrzNxFprKrHfRxbXvNmXwwD3lXVl0Wk\nHe7ntxqrqsv34fmVHB03/fWKwsp/XODNvkBEegBPADeq6tk8ii2vXW5fFAMaA7+KyG7cbbBzg7RD\n29u/kW9UNVVVdwHbcCeOYOPNvrgd+BRAVZcBEbgLBuY3Xh1PMvPXRGHlPy647L7wNLdMw50kgrUd\nGi6zL1Q1UVXLqmoNVa2Bu7/mRlXNcTE0P+bN38jXuG90QETK4m6Kis3TKPOGN/siDugOICINcCeK\nw3kapX+YC4z23P3UFkhU1QOX+5BfNj2p78p/BBwv98W/gaLAZ57+/DhVvdGxoH3Ey32RL3i5L+YD\nvURkM5AOPKKqR5yL2je83BcPATNE5AHcTS1jg/HEUkQ+xt3UWNbTH/MUEA6gqlNx98/0A2KAM8A4\nr7YbhPvKGGNMLvLXpidjjDF+whKFMcaYbFmiMMYYky1LFMYYY7JlicIYY0y2LFEYvyMi6SKyPsOr\nRjbr1rhUpcwr/M5fPdVHN3hKXtTLwTbuFJHRnvdjReSaDMtmikjDXI5zlYg09+IzfxWRwlf73Sb/\nskRh/FGSqjbP8NqdR987QlWb4S42+e8r/bCqTlXV9z2TY4FrMiyboKqbcyXKC3G+hXdx/hWwRGFy\nzBKFCQieK4ffRGSt59U+i3UaichKz1VIlIjU9cwfmWH+NBEJvczXLQbqeD7b3TOGwUZPrf+CnvnP\ny4UxQF7yzPuHiDwsIrfirrn1oec7C3muBCJF5C4ReTFDzGNF5PUcxrmMDAXdRORtEVkt7rEn/umZ\ndx/uhLVQRBZ65vUSkWWe/fiZiBS9zPeYfM4ShfFHhTI0O33lmfcH0FNVWwJDgNey+NydwKuq2hz3\ngTreU65hCNDBMz8dGHGZ778B2CgiEcC7wBBVbYK7ksFdIlIauBlopKpNgWcyflhVPwdW4z7zb66q\nSRkWfw7ckmF6CPBJDuPsg7tMxzlPqGok0BToIiJNVfU13LV8uqlqN08pjyeBHp59uRp48DLfY/I5\nvyzhYfK9JM/BMqNw4A1Pm3w67rpFmS0DnhCRKsCXqrpDRLoDrYBVnvImhXAnnax8KCJJwG7cZajr\nAbtUdbtn+XvAPcAbuMe6mCki/wO8LmmuqodFJNZTZ2eH5zuWerZ7JXEWwV2uIuMIZYNFZBLuv+tK\nuAfoicr02bae+Us931MA934z5pIsUZhA8QBwCGiG+0r4T4MSqepHIrIC6A/MF5EJuMsqv6eqj3vx\nHSMyFhAUkSzHN/HUFmqNu8jcUOBe4Por+Fk+AQYDW4GvVFXFfdT2Ok7co7g9D7wJ3CIiNYGHgetU\n9ZiIvIu78F1mAvyoqsOuIF6Tz1nTkwkUJYADnvEDRuE+m76IiNQCYj3NLXNxN8H8DNwqIuU965QW\n78cU3wrUEJE6nulRwCJPm34JVZ2Hu6M4qzuPTuIue56VL4GbcI+R8Iln3hXFqaqpuJuQ2nqarYoD\np4FEEakA9L1ELMuBDud+JhEpLCJZXZ0Zc54lChMo3gLGiMhy3M1Op7NYZwiwSUTWA/VxD/m4GfcB\ndYGIRAE/4m6WuSxVTcZdXfMzEdkIuICpuA+633m2twj31U5m7wJTz3VmZ9ruMWAzUF1VV3rmXXGc\nnr6Pl4GHVXUD7vGxo4HZuJuzzpkOfC8iC1X1MO47sj72fM9y3PvKmEuy6rHGGGOyZVcUxhhjsmWJ\nwhhjTLYsURhjjMmWJQpjjDHZskRhjDEmW5YojDHGZMsShTHGmGz9f46lVyUVBbS2AAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = tree_model.predict_proba(X_test)[:, 1]\n",
    "make_auc_plot(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "u7NysoFMZByn"
   },
   "source": [
    "## Hyperparameter Tuning\n",
    "\n",
    "The last step on our journey is to conduct some hyperparameter tuning. We have several parameters that we can set in our Decision Tree, and it is not always obvious what we should set them to. We can use a grid search to find the best model after we change many different parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "wTc6NGaLXGX5"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "jtQffaFRXSfd"
   },
   "outputs": [],
   "source": [
    "# initialize our model with the default parameters\n",
    "tree_model = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "1PCphaqmXNPO"
   },
   "outputs": [],
   "source": [
    "# set up a dictionary with lists for the different parameter values\n",
    "param_grid = {'min_samples_leaf': [1, 2, 10, 20, 100], 'min_impurity_decrease': [0.0, 0.1, 0.2]}\n",
    "# initialize our grid searcher\n",
    "grid_searcher = GridSearchCV(tree_model, param_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 258
    },
    "colab_type": "code",
    "id": "-kDSQ_0zX4SD",
    "outputId": "79e2c9a4-bd00-4356-9fec-e2102b51e1ba"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best'),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'min_samples_leaf': [1, 2, 10, 20, 100], 'min_impurity_decrease': [0.0, 0.1, 0.2]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_searcher.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "PlG7pjrcX-E8"
   },
   "outputs": [],
   "source": [
    "# get our best model\n",
    "tree_model = grid_searcher.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "colab_type": "code",
    "id": "aVcT1-1qYFAf",
    "outputId": "c87abbdc-37a4-4c38-80cd-b220808dbb66"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': None,\n",
       " 'max_leaf_nodes': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_impurity_split': None,\n",
       " 'min_samples_leaf': 100,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'presort': False,\n",
       " 'random_state': None,\n",
       " 'splitter': 'best'}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_model.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "yU50TI01YrsY",
    "outputId": "a26cda05-1e80-48e9-8b2a-9f754b5fb858"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WYgnC+LfMRfw6dOhA/fr1eeCBBwgNLZzjZ5YoTIFbsec4r/60i2W7jlGtXHFmj42ge+Mq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\nrkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = tree_model.predict_proba(X_test)[:, 1]\n",
    "make_auc_plot(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rXtmYitsR4dK"
   },
   "source": [
    "# Group time!\n",
    "\n",
    "Some ideas:\n",
    "\n",
    "1. Explore more feature engineering and more features\n",
    "2. Explore other model types:\n",
    "    - ensemble methods: either gradient boosted classifiers or random forests\n",
    "    - GAMs (https://github.com/dswah/pyGAM)\n",
    "    - other classifiers (https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html)\n",
    "3. Explore the data (or model) using visualizations\n",
    "4. Do so clustering analysis of the data\n",
    "5. Mix and match from above\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ri4B5X9UR5Ow"
   },
   "source": [
    "## A note on making plots\n",
    "\n",
    "If there is one thing I have learned in Astro grad school it is how to make good plots, things like: labeling axes, having clear style, and choosing the right plot type. These small things will make a big difference when presenting your work to a wide array of audiences.\n",
    "\n",
    "So you want to make some plots...\n",
    "\n",
    "What type of plot do you want to make? If you need inspiration or want to see different options you can check out the sites below.\n",
    "\n",
    "https://www.data-to-viz.com/\n",
    "\n",
    "https://datavizcatalogue.com/index.html\n",
    "\n",
    "http://datavizproject.com/\n",
    "\n",
    "And see some examples of common plots made in different python libraries.\n",
    "\n",
    "http://pythonplot.com/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import xgboost\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "use_cols = [\n",
    "    'Age', 'WorkClass', 'EducationNum', 'MaritalStatus', 'Occupation', 'Relationship', 'Race', 'Gender',\n",
    " 'CapitalGain', 'CapitalLoss', 'HoursPerWeek', 'NativeCountry'\n",
    "]\n",
    "X = pd.get_dummies(df[use_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=432345)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=5, min_child_weight=1, missing=None, n_estimators=250,\n",
       "       n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=True, subsample=1)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = XGBClassifier(n_estimators=250, max_depth=5)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = model.predict_proba(X_test)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Bl22iEJGWwEygDFBdRBoDY1T1d9m9VlUXAAvSrftjmucKPOR9+EVSioe1Px9H\nBCqWtKs/THBKW8SvR48etG/fnvvuu4+QEH92Chjj8OW3bArQCzgGoKobCKIy45+u288vpxJ49NYb\n3A7FmFz56aef6NChAzNnzgQgJiaG8ePHW5Iw+caX37QQVd2Xbl2KP4Lxh883HqBiyaLc06G226EY\nkyNJSUlMmjSJxo0bs3XrVkqWtNpkxh2+jFHEebuf1Hu39e+AoJgKdcsvp1i64wg9GlYiJMTmxDbB\nY/369YwcOZL169fTv39//vnPf1KpklU7Nu7wJVHci9P9VB04BHzlXRfwNsafAmB4m5ruBmJMDh08\neJCDBw/y8ccfc8cd2RVBMMa/fEkUyao6yO+R+MF3O48QGiI0qVbW7VCMydayZcvYuHEj9913H926\ndWP37t1ERES4HZYxPo1RrBaRBSISIyKl/B5RHvF4lO92HOW6q0paXScT0M6cOcP48eNp3749r7zy\nSmoRP0sSJlBkmyhUtTbwLNAc2CQin4lIwJ9h7DpyljMXk7m1/jVuh2JMphYuXEiDBg147bXXeOCB\nB/jxxx+tiJ8JOD5dX6eq36vqBKAZcBpnQqOA9t+NBwDo3cTmnTCBKS4ujl69ehEREcGyZct45ZVX\n7MomE5CyTRQiUlJE7hKR+cAq4AgQ8PUCvtlxhBJFQm26UxNQVJVVq1YBUK1aNb744gvWrVtnJThM\nQPPljGIz0Bp4UVWvU9WHVXWln+O6IsfPJbIh7iStr7UqsSZwHDhwgH79+tGqVavUIn4333yzFfEz\nAc+Xq56uVVWP3yPJQ19sdrqdhrWp4XIkxjhnEW+99RYPPfQQCQkJvPDCC7Rt29btsIzxWaaJQkRe\nVtWHgY9F5DdzQPgww51r9hw5B9i8EyYwDBgwgDlz5tC+fXtmzJjB9ddf73ZIxuRIVmcUH3r/zenM\ndq5KSEph5rK9tKxZnuJF7LJY446UlBREhJCQEG677TY6d+7M3XffbfWZTFDK9LdWVVd5n9ZT1cVp\nH0C9/Akv55753JmSu1p5uwbduGPbtm20b98+tYjf8OHDuffeey1JmKDly2/uqAzWjc7rQPJC3PHz\nvLcyloaRZXjpzkZuh2MKmaSkJJ599lmaNGnC9u3bKVOmjNshGZMnshqjGIgzK10tEfkkzaZSwMmM\nX+Wuj9bEAc4ERSJWBNDkn3Xr1jFixAg2btzIwIEDmTJlCldffbXbYRmTJ7Iao1iFMwdFVeDVNOvP\nAOv8GVRuvbPCqYYeVaW0y5GYwubQoUMcPXqUzz77jD59+rgdjjF5KtNEoap7gb041WID3oFTFzhx\nPonuDSpRsqjPM7wak2tLly4/PuNGAAAaBElEQVRl06ZN3H///XTr1o1du3ZRvHhxt8MyJs9lOkYh\nIt96/z0hIsfTPE6IyPH8C9E3M7/bC8DItrVcjsQUdKdPn+a+++6jY8eOTJkyJbWInyUJU1BlNZh9\nabrTisBVaR6XlgPKzsNnKRcRToua5dwOxRRgCxYsoH79+kybNo2HHnrIiviZQiGry2Mv3Y1dDQhV\n1RSgDXA3UCIfYsuRnYfO0L7OVTaIbfwmLi6OPn36UKZMGb7//ntefvllSpQIuD8FY/KcL5fHfoYz\nDWpt4N8491C859eocuFMQjIVS9o3O5O3VJUVK1YAThG/RYsW8eOPP9KqVSuXIzMm//iSKDyqmgTc\nAbyiqr8DAqp2d1KKhzMXkylexG5oMnnnl19+oW/fvrRp0ya1iF+nTp0oUqSIy5EZk798+WRNFpE7\ngWHA59514f4LKeeOnnUGE4uEWskOc+VUlRkzZhAVFcWiRYt46aWXrIifKdR8uY50FHAfTpnxPSJS\nC3jfv2HlzKUrnprVsLmxzZXr378/n3zyCR07dmTGjBlcd911bodkjKuyTRSqullEJgDXiUhdYJeq\n/tX/ofnure9/pkzxcNrWruh2KCZIpS3i17dvX7p27crYsWOtPpMx+DbDXXtgFzATmAXsEJGAOQ+/\nkJhCskfp3bgKISF2xZPJuc2bN9O2bdvUIn7Dhg2zSq/GpOHLX8LfgR6q2lZVbwR6Av/wb1i+23vU\nmXuiRgWrFmtyJjExkT//+c80a9aM3bt3U66c3YNjTEZ8GaMooqpbLy2o6jYRCZjLPtbsc24Sj6ps\n9Z2M79auXcuIESPYvHkzQ4YM4ZVXXuGqqwLuPlJjAoIvieJHEZkGvONdvosAKgr43spYAOpXsZLO\nxnfHjh3j5MmTzJ8/n169erkdjjEBzZdEcQ8wAXgMEGAp8E9/BuWrhKQUfjp4ht6Nq1AmIqCu2DUB\naMmSJWzatIkJEybQtWtXdu7cSbFixdwOy5iAl+UYhYg0BLoBn6pqb1W9TVX/pqoJ+RNe1qYs3glA\nu+vsaieTuVOnTnH33XfTuXNnXn/99dQifpYkjPFNVtVj/4BTvuMu4H8iktFMd676+ZgzkN2veVWX\nIzGBav78+URFRTFjxgweeeQR1q5da0X8jMmhrLqe7gIaqeo5EbkKWIBzeWzAOHk+ieLhoYTaZbEm\nA3FxcfTr14+6devy2Wef0aJFC7dDMiYoZdX1dFFVzwGo6pFs2roiRIRaFa16p7lMVfn++++By0X8\n1qxZY0nCmCuQ1Yf/tSLyiffxKVA7zfInWbwulYh0E5HtIrJLRB7Pol1/EVERic5J8OcSkylfImCu\n1DUui4+Pp3fv3rRt2za1iN9NN91kRfyMuUJZdT31S7f8r5zsWERCcebavgWIB1aLyLy092R425XC\nuapqZU727/Eo2w6c5vamNj5R2Hk8Ht544w0effRRkpOTmTx5Mu3atXM7LGMKjKzmzF58hftuiVMX\nag+AiHwA9AG2pmv3F+BF4JGc7Hz1z8dJSPJQr3KpKwzTBLt+/frx2Wef0blzZ9544w2uvfZat0My\npkDx57hDJBCXZjmedPNYiEhToJqqfk4WRGSciKwRkTVHjhwB4MT5JAAaVbWKsYVRcnIyHo8zCWO/\nfv144403+OqrryxJGOMH/kwUGV2KpKkbRUJw6kg9nN2OVHW6qkaravSlMgtnEpxEUT7C+p8Lm40b\nN9KmTRveeOMNAIYOHcqYMWNsGlxj/MTnRCEiOb34PB5nvu1LqgK/pFkuBTQAvhGRn4HWwDxfB7RX\n7HFqPFUoaYmisLh48SJPP/00zZs3Z9++fVabyZh84kuZ8ZYisgnY6V1uLCK+lPBYDdQRkVreIoKD\ngHmXNqrqKVWtqKo1VbUmsALoraprstvx+cRkPv4xnooli1KiqC9VSEywW716Nc2aNeOZZ55h8ODB\nbNu2jTvuuMPtsIwpFHz5lJ0C9MK5SxtV3SAinbJ7kaomi8h4YCEQCsxS1S0i8gywRlXnZb2HzK3+\n+QQA991UO7e7MEHmxIkTnD17lgULFtC9e3e3wzGmUPElUYSo6r50/b8pvuxcVRfg3NGddt0fM2l7\nky/7BFi45SAAtzWu4utLTBD6+uuv2bRpEw888ABdu3Zlx44dVn7DGBf4MkYRJyItARWRUBH5PbDD\nz3FlKTnFudrlqlL2oVEQnTx5krFjx9KlSxemTZuWWsTPkoQx7vAlUdwLPARUBw7hDDrf68+gsrNp\n/2la1LTZyAqiuXPnEhUVxaxZs3jsscesiJ8xASDbridVPYwzEB0wth04Tc+Gld0Ow+Sx2NhY7rzz\nTurVq8e8efOIjs5RRRdjjJ9kmyhE5A3S3P9wiaqO80tE2UhMdrqd6layO7ILAlVl2bJltG/fnurV\nq/PVV1/RunVrq89kTADxpevpK2Cx97EcuBq46M+gspLkHZ+oYVVjg15sbCw9e/akQ4cOqUX8OnTo\nYEnCmADjS9fTh2mXReQd4H9+iygbCd4ziurlI9wKwVwhj8fD1KlTmThxIqrKlClTrIifMQEsN3er\n1QJq5HUgvkrxKCFAtXLF3QrBXKE77riDuXPncssttzB9+nRq1qzpdkjGmCz4MkZxgstjFCHAcSDT\nuSX8LTnFQxGgdPFwt0IwuZCcnExISAghISEMHDiQPn36MGLECKvPZEwQyDJRiPNX3BjY713lUdXf\nDGznp8RkDxWKhREeGnAT7plMbNiwgVGjRjF27FjuueceBg8e7HZIxpgcyPLT1psUPlXVFO/D1SQB\nkOxRUjyuh2F8kJCQwFNPPUV0dDTx8fFUqlTJ7ZCMMbngyxjFKhFppqo/+j0aHyQkpXBj7Ypuh2Gy\nsWrVKmJiYvjpp5+IiYlh8uTJlC9f3u2wjDG5kGmiEJEwVU0G2gFjRWQ3cA5nnglV1Wb5FGP6uPC4\nf2JjsnH69GkuXLjAl19+ya233up2OMaYK5DVGcUqoBnQN59i8YlH1aY/DVCLFi1iy5YtPPjgg9x8\n881s377dym8YUwBkNUYhAKq6O6NHPsWXofOJPhWvNfnkxIkTjBw5kltvvZWZM2daET9jCpisziiu\nEpGHMtuoqpP9EI9Prr/GzigCxSeffML999/PkSNHeOKJJ/jjH/9oCcKYAiarRBEKlCTjua9dVTTM\nLo0NBLGxsQwaNIgGDRqwYMECmjZt6nZIxhg/yCpRHFDVZ/ItkhwoGhbqdgiFlqqydOlSOnbsSPXq\n1fn6669p1aoV4eF2A6QxBVW2YxSBqFyEfSi5Yd++fXTv3p2bbroptYhfu3btLEkYU8BllSi65FsU\nOVQ2wqqL5iePx8O//vUv6tevz7Jly/jnP/9J+/bt3Q7LGJNPMu16UtXj+RlITlQuU8ztEAqVvn37\nMn/+fG699VamTZtGjRqu1YQ0xrggN9VjXRdR1MYo/C0pKYnQ0FBCQkIYPHgw/fv3Z9iwYVbEz5hC\nKCgvHypiBQH96scff6Rly5ZMnToVgMGDBzN8+HBLEsYUUkH3iStgH1h+cuHCBZ544glatmzJwYMH\nqVatmtshGWMCQNB1PYVYkvCLFStWEBMTw44dOxg1ahQvvfQS5cqVczssY0wACLpEEbgX7Qa3c+fO\nkZSUxP/+9z9uvvlmt8MxxgQQCYApJnIkIvJ6Pb9/h9thFAhffvklW7Zs4eGHHwYgMTGRIkXs0mNj\nCiIRWauq0bl5bdCNUZgrd+zYMWJiYujevTtvv/02iYmJAJYkjDEZskRRiKgqc+bMISoqivfee4+n\nnnqK1atXW4IwxmQp+MYoTK7FxsYyZMgQGjVqxKJFi2jcuLHbIRljgoCdURRwqsrXX38NQI0aNfjm\nm29YsWKFJQljjM+CLlHYRU++27t3L127dqVLly6pRfxuvPFGwsLsRNIY47ugSxQmeykpKfzjH/+g\nQYMGrFy5ktdff92K+Bljcs2+WhZAffr04b///S89evRg6tSpdoe1MeaKBN19FCUir9dzdh/Fb6Qt\n4vfhhx+SnJzMkCFDrNyJMQYI4PsoRKSbiGwXkV0i8ngG2x8Ska0islFEFouI1a/OhTVr1hAdHc3r\nr78OwMCBA7nrrrssSRhj8oTfEoWIhAKvAt2BKGCwiESla7YOiFbVRsAc4EV/xVMQXbhwgYkTJ9Kq\nVSuOHDli80QYY/zCn2cULYFdqrpHVROBD4A+aRuo6hJVPe9dXAFU9WM8BcoPP/xA48aNefHFFxk1\nahRbt26lV69ebodljCmA/DmYHQnEpVmOB1pl0X408EVGG0RkHDAOoFil2nkVX1C7cOECHo+Hr776\nii5dAnbWWmNMAeDPRJFRB3mGI+ciMhSIBjpmtF1VpwPTwRnMzqsAg82CBQvYsmULjz76KJ07d2bb\ntm2Eh4e7HZYxpoDzZ9dTPJD2usyqwC/pG4nIzcCTQG9VvejHeILW0aNHGTp0KD179uTdd99NLeJn\nScIYkx/8mShWA3VEpJaIFAEGAfPSNhCRpsA0nCRx2I+xBCVV5YMPPqBevXrMnj2bp59+mlWrVlkR\nP2NMvvJb15OqJovIeGAhEArMUtUtIvIMsEZV5wF/A0oCH3kv5YxV1d7+iinYxMbGEhMTQ+PGjZk5\ncyYNGzZ0OyRjTCFkN9wFGFVl8eLFqbPMrVixghYtWhAaGupyZMaYYBawN9z5gxTgsoC7d++mS5cu\n3HLLLalF/Fq3bm1JwhjjqqBLFAVRSkoKkydPpmHDhqxdu5Zp06ZZET9jTMAIvqKABfCE4rbbbuOL\nL76gV69evP7661StavcdGmMCR9CNUZSseoOejd/udhhXLDExkbCwMEJCQpg9ezYpKSkMGjTI6jMZ\nY/yiUI1RFASrVq2iefPmvPbaawAMGDCAwYMHW5IwxgQkSxT56Pz58zz88MO0adOGEydOULu2lSMx\nxgS+4BujCFLLli0jJiaGPXv2cPfdd/PCCy9QpkwZt8MyxphsWaLIJ5cmFlqyZAk33XST2+EYY4zP\nbDDbj+bPn8+2bdt47LHHAEhOTiYszHKzMSb/2WB2gDly5AhDhgyhd+/evP/++6lF/CxJGGOCkSWK\nPKSqvPfee9SrV485c+bwzDPPsHLlSiviZ4wJavYVNw/FxsYycuRImjZtysyZM6lfv77bIRljzBWz\nM4or5PF4WLhwIQA1atTgu+++Y/ny5ZYkjDEFRtAlikC6JW3nzp107tyZbt26sXTpUgBatmxpRfyM\nMQVK0CWKQJCcnMzf/vY3GjVqxPr165k5c6YV8TPGFFg2RpELvXr1YuHChfTp04fXXnuNKlWquB2S\nMcb4TdDdR1Gq6g16xoX7KC5evEh4eDghISHMmTMHj8fDnXfeafWZjDFBwe6j8LMVK1bQrFkzXn31\nVQD69+/PgAEDLEkYYwoFSxRZOHfuHA8++CA33ngjZ86coU6dOm6HZIwx+c7GKDLx3XffERMTw969\ne7nvvvt47rnnKF26tNthGWNMvrNEkYnk5GTCw8P59ttv6dChg9vhGGOMa4JvMLvaDXomzj+D2Z99\n9hnbtm3jiSeeAKyInzGm4LDB7Ct06NAhBgwYwO23386cOXOsiJ8xxqRRqBOFqvLOO+8QFRXF3Llz\n+etf/8qKFSusiJ8xxqQRdF+Z8/KC1NjYWMaMGUN0dDQzZ86kbt26ebh3Y4wpGArdGYXH4+GLL74A\nnCJ+y5cvZ+nSpZYkjDEmE4UqUezYsYObbrqJHj168O233wIQHR1tRfyMMSYLhSJRJCcn88ILL9Co\nUSM2bdrEm2++aZe8GmOMj4JujCI3evbsyaJFi7jjjjt49dVXqVSpktshGWNM0Ai6+yhKV7tBT/tw\nH0VCQgLh4eGEhoby8ccfA9CvXz9/h2eMMQHJ7qNIZ/ny5TRp0iS1iF+/fv0sSRhjTC4VqERx9uxZ\nJkyYQPv27UlISKBevXpuh2SMMUGvwIxRfPvtt8TExBAbG8v48eOZNGkSJUuWdDssY4wJegUmUQBE\nRETw3Xff0bZtW7dDMcaYAiOoB7M/+eQTfvrpJ/7whz8AkJKSYvdEGGNMBgJ2MFtEuonIdhHZJSKP\nZ7C9qIh86N2+UkRq+rLfgwcP0r9/f/r168enn36aWsTPkoQxxuQ9vyUKEQkFXgW6A1HAYBGJStds\nNHBCVa8D/g68kN1+k86dpl69enz++ec899xzfP/991bEzxhj/MifZxQtgV2qukdVE4EPgD7p2vQB\n3vY+nwN0kWwmok44cYgGDRqwYcMGHn/8ccLDw/M8cGOMMZf5czA7EohLsxwPtMqsjaomi8gpoAJw\nNG0jERkHjPMuXly2bNlmK+IHQEXSHatCzI7FZXYsLrNjcdkNuX2hPxNFRmcG6UfOfWmDqk4HpgOI\nyJrcDsgUNHYsLrNjcZkdi8vsWFwmImty+1p/dj3FA9XSLFcFfsmsjYiEAWWA436MyRhjTA75M1Gs\nBuqISC0RKQIMAualazMPiPE+7w98rcF2va4xxhRwfut68o45jAcWAqHALFXdIiLPAGtUdR4wE3hH\nRHbhnEkM8mHX0/0VcxCyY3GZHYvL7FhcZsfislwfi6C74c4YY0z+KlBFAY0xxuQ9SxTGGGOyFLCJ\nwl/lP4KRD8fiIRHZKiIbRWSxiNRwI878kN2xSNOuv4ioiBTYSyN9ORYiMsD7u7FFRN7L7xjziw9/\nI9VFZImIrPP+nfRwI05/E5FZInJYRDZnsl1EZIr3OG0UkWY+7VhVA+6BM/i9G7gWKAJsAKLStbkP\nmOp9Pgj40O24XTwWnYAI7/N7C/Ox8LYrBSwFVgDRbsft4u9FHWAdUM67fLXbcbt4LKYD93qfRwE/\nux23n45FB6AZsDmT7T2AL3DuYWsNrPRlv4F6RuGX8h9BKttjoapLVPW8d3EFzj0rBZEvvxcAfwFe\nBBLyM7h85suxGAu8qqonAFT1cD7HmF98ORYKlPY+L8Nv7+kqEFR1KVnfi9YH+Lc6VgBlRaRydvsN\n1ESRUfmPyMzaqGoycKn8R0Hjy7FIazTON4aCKNtjISJNgWqq+nl+BuYCX34vrgeuF5HlIrJCRLrl\nW3T5y5dj8SdgqIjEAwuA3+VPaAEnp58nQOBOXJRn5T8KAJ9/ThEZCkQDHf0akXuyPBYiEoJThXhE\nfgXkIl9+L8Jwup9uwjnL/E5EGqjqST/Hlt98ORaDgbdU9WURaYNz/1YDVfX4P7yAkqvPzUA9o7Dy\nH5f5ciwQkZuBJ4Heqnoxn2LLb9kdi1JAA+AbEfkZpw92XgEd0Pb1b2Suqiap6l5gO07iKGh8ORaj\ngdkAqvoDUAynYGBh49PnSXqBmiis/Mdl2R4Lb3fLNJwkUVD7oSGbY6Gqp1S1oqrWVNWaOOM1vVU1\n18XQApgvfyOf4VzogIhUxOmK2pOvUeYPX45FLNAFQETq4SSKI/kaZWCYBwz3Xv3UGjilqgeye1FA\ndj2p/8p/BB0fj8XfgJLAR97x/FhV7e1a0H7i47EoFHw8FguBriKyFUgBHlXVY+5F7R8+HouHgTdE\n5EGcrpYRBfGLpYi8j9PVWNE7HvM0EA6gqlNxxmd6ALuA88BIn/ZbAI+VMcaYPBSoXU/GGGMChCUK\nY4wxWbJEYYwxJkuWKIwxxmTJEoUxxpgsWaIwAUdEUkRkfZpHzSza1sysUmYO3/Mbb/XRDd6SFzfk\nYh/3iMhw7/MRIlIlzbYZIhKVx3GuFpEmPrzm9yIScaXvbQovSxQmEF1Q1SZpHj/n0/vepaqNcYpN\n/i2nL1bVqar6b+/iCKBKmm1jVHVrnkR5Oc7X8C3O3wOWKEyuWaIwQcF75vCdiPzofdyYQZv6IrLK\nexayUUTqeNcPTbN+moiEZvN2S4HrvK/t4p3DYJO31n9R7/rn5fIcIC951/1JRB4Rkf44Nbfe9b5n\nce+ZQLSI3CsiL6aJeYSI/DOXcf5AmoJuIvK6iKwRZ+6JP3vXTcBJWEtEZIl3XVcR+cF7HD8SkZLZ\nvI8p5CxRmEBUPE2306fedYeBW1S1GTAQmJLB6+4B/qGqTXA+qOO95RoGAm2961OAu7J5/9uATSJS\nDHgLGKiqDXEqGdwrIuWB24H6qtoIeDbti1V1DrAG55t/E1W9kGbzHOCONMsDgQ9zGWc3nDIdlzyp\nqtFAI6CjiDRS1Sk4tXw6qWonbymPp4CbvcdyDfBQNu9jCrmALOFhCr0L3g/LtMKBf3n75FNw6hal\n9wPwpIhUBT5R1Z0i0gVoDqz2ljcpjpN0MvKuiFwAfsYpQ30DsFdVd3i3vw3cD/wLZ66LGSLyX8Dn\nkuaqekRE9njr7Oz0vsdy735zEmcJnHIVaWcoGyAi43D+rivjTNCzMd1rW3vXL/e+TxGc42ZMpixR\nmGDxIHAIaIxzJvybSYlU9T0RWQn0BBaKyBicsspvq+oTPrzHXWkLCIpIhvObeGsLtcQpMjcIGA90\nzsHP8iEwAPgJ+FRVVZxPbZ/jxJnF7XngVeAOEakFPAK0UNUTIvIWTuG79AT4n6oOzkG8ppCzricT\nLMoAB7zzBwzD+Tb9KyJyLbDH290yD6cLZjHQX0Su9rYpL77PKf4TUFNErvMuDwO+9fbpl1HVBTgD\nxRldeXQGp+x5Rj4B+uLMkfChd12O4lTVJJwupNbebqvSwDnglIhcA3TPJJYVQNtLP5OIRIhIRmdn\nxqSyRGGCxWtAjIiswOl2OpdBm4HAZhFZD9TFmfJxK84H6iIR2Qj8D6dbJluqmoBTXfMjEdkEeICp\nOB+6n3v39y3O2U56bwFTLw1mp9vvCWArUENVV3nX5ThO79jHy8AjqroBZ37sLcAsnO6sS6YDX4jI\nElU9gnNF1vve91mBc6yMyZRVjzXGGJMlO6MwxhiTJUsUxhhjsmSJwhhjTJYsURhjjMmSJQpjjDFZ\nskRhjDEmS5YojDHGZOn/AU2m1QULPhGSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_auc_plot(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "DataScienceWorkshop.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
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