MAIN-FNTA.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.9"},"colab":{"name":"MAIN-FNTA.ipynb","provenance":[{"file_id":"1I9_bR5sSqXD_2pl37Lo9388uxhwepjWj","timestamp":1578577799773},{"file_id":"1ZYMx2KM2Wm2zNMrT8V-BK1hS7G6uIeLI","timestamp":1578577587881},{"file_id":"1Enj1MRBf9zztCyLRu3H_BrwOYuIRKF-H","timestamp":1578577259020},{"file_id":"1KZz8wiu0Z0O4LL-9F17OSnOHqBnKfirA","timestamp":1578575767345}],"collapsed_sections":[],"toc_visible":true},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"k7bGX7_W-Jgz","colab_type":"text"},"source":["# Machine Learning - FNTA\n"]},{"cell_type":"markdown","metadata":{"id":"OJwynar_-Jg0","colab_type":"text"},"source":["**By Jakke Neiro & Andrei Roibu** "]},{"cell_type":"markdown","metadata":{"id":"VvWpbvp0-Jg1","colab_type":"text"},"source":["## 1. Importing All Required Dependencies"]},{"cell_type":"markdown","metadata":{"id":"jAgxUNM--Jg1","colab_type":"text"},"source":["This script imports all the required dependencies for running the different functions and the codes. Also, by using the _run_ command, the various notebooks are imprted into the main notebook."]},{"cell_type":"code","metadata":{"id":"3-7jZj5s-Jg2","colab_type":"code","colab":{}},"source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","from scipy import interp\n","\n","import glob, os\n","\n","from sklearn.metrics import accuracy_score, roc_curve, roc_auc_score, auc, roc_auc_score, confusion_matrix, classification_report, log_loss\n","from sklearn.model_selection import train_test_split\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis\n","\n","from sklearn import svm, datasets, tree\n","from sklearn.ensemble import RandomForestClassifier\n","\n","\n","from sklearn.neural_network import MLPClassifier, MLPRegressor\n","from sklearn.preprocessing import StandardScaler\n"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"sXIMsovL_M6A","colab_type":"text"},"source":["In order to take advantage of the speed increases provided by GPUs, this code has been modified in order to run on Google Colab notebooks. In order to do this, the user needs to set the *google_colab_used* parameter to **True**. For usage on a local machine, this needs to be set to **False**\n","\n","If used on a google colab notebook, the user will need to follow the instructions to generate and then copy an autorisation code from a generated link.\n"]},{"cell_type":"code","metadata":{"id":"qQmNwp-E-Jg4","colab_type":"code","outputId":"21ecafb1-5539-4169-de3e-86b4bc0bf935","executionInfo":{"status":"ok","timestamp":1578578478408,"user_tz":0,"elapsed":21027,"user":{"displayName":"Andrei Roibu","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDUCsIj5qwCoMqexSria42B1d9boiojvcfQgzmzXA=s64","userId":"06542274079142548932"}},"colab":{"base_uri":"https://localhost:8080/","height":122}},"source":["google_colab_used = True\n","\n","if google_colab_used == True:\n","  # Load the Drive helper and mount\n","  from google.colab import drive\n","\n","  # This will prompt for authorization.\n","  drive.mount('/content/drive')\n","\n","  data_drive = '/content/drive/My Drive/JA-ML/data'\n","\n","  os.chdir(data_drive)\n","\n","else:\n","  os.chdir(\"./data\")"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"5bf89YRW-Jg6","colab_type":"text"},"source":["## 2. Data Pre-Processing"]},{"cell_type":"markdown","metadata":{"id":"Q7Yw28Yf-Jg6","colab_type":"text"},"source":["This section imports all the required datasets as pandas dataframes, concatenates them, after which it pre-processes them by eliminating all non-numerical data and columns which contain the same data-values. This script also creates the input dataset and the labeled output dataset."]},{"cell_type":"code","metadata":{"id":"x5t04I1u-Jg7","colab_type":"code","colab":{}},"source":["def data_preprocessing():\n","    \n","    '''\n","    This reads all the input datasets, pre-processes them and then generates the input dataset and the labelled dataset.\n","    \n","    Args:\n","        None\n","        \n","    Returns:\n","        X (ndarray): A 2D array containing the input processed data\n","        y (ndarray): A 1D array containing a list of labels, with 1 corresponding to \"active\" and 0 corresponding to \"dummy\"\n","        \n","    '''\n","    \n","    df_list = []\n","    y = np.array([])\n","    for file in glob.glob(\"fnta*.csv\"):\n","        df = pd.read_csv(file, header = 0)\n","\n","        cols = df.shape[0]\n","        if \"actives\" in file:\n","            y_df = np.ones((cols))\n","        else:\n","            y_df = np.zeros((cols))\n","        y = np.concatenate((y,y_df), axis=0)\n","\n","        df_list.append(df)\n","\n","    global_df = pd.concat(df_list, axis=0, ignore_index=True)\n","    global_df = global_df._get_numeric_data() # removes any non-numeric data\n","    global_df = global_df.loc[:, (global_df != global_df.iloc[0]).any()] # modifies the dataframe to remove columns with only 0s\n","\n","    X_headers = list(global_df.columns.values)\n","    X = global_df.values\n","    \n","    return X,y"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"RRnDX4lb-Jg8","colab_type":"code","colab":{}},"source":["X,y = data_preprocessing()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"k_1tZdOg-Jg-","colab_type":"code","colab":{}},"source":["def data_split(X,y,random_state=42):\n","    \n","    '''\n","    This function takes the original datasets and splits them into training and testing datasets. For consistency, the function employs a 80-20 split for the train and test sets.\n","    \n","    Args:\n","        X (ndarray): A 2D array containing the input processed data\n","        y (ndarray): A 1D array containing a list of labels, with 1 corresponding to \"active\" and 0 corresponding to \"dummy\"\n","        random_state (int): An integer, representing the seed to be used by the random number generator; if not provided, the default value goes to 42\n","        \n","    Returns:\n","        X_train (ndarray): 2D array of input dataset used for training\n","        X_test (ndarray): 2D array of input dataset used for testing\n","        y_train (ndarray): 1D array of train labels \n","        y_test (ndarray): 1D array of test labels  \n","        \n","    '''\n","\n","    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=random_state)\n","    \n","    return X_train, X_test, y_train, y_test"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"LSvu6ici-Jg_","colab_type":"code","colab":{}},"source":["X_train, X_test, y_train, y_test = data_split(X,y)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"T296y19l-JhB","colab_type":"text"},"source":["## 3. Model Evaluation\n"]},{"cell_type":"markdown","metadata":{"id":"4dO9U2j_-JhB","colab_type":"text"},"source":["This section produces the ROC plot, as well as several other performance metrics, including the classifier scores, the log-loss for each classifier, the confusion matrix and the classification report including the f1 score. The f1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0."]},{"cell_type":"code","metadata":{"id":"lqJmWYFm-JhC","colab_type":"code","colab":{}},"source":["def ROC_plotting(title, y_test, y_score):\n","    \n","    '''\n","    This function generates the ROC plot for a given model.\n","    \n","    Args:\n","        title (string): String represending the name of the model.\n","        y_test (ndarray): 1D array of test dataset \n","        y_score (ndarray): 1D array of model-predicted labels\n","        \n","    Returns:\n","        ROC Plot\n","        \n","    '''\n","    \n","\n","    fpr, tpr, _ = roc_curve(y_test, y_score)\n","    roc_auc = auc(fpr, tpr)\n","        \n","    plt.figure()\n","    lw = 2 # linewidth\n","    plt.plot(fpr, tpr, color='darkorange', lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n","    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n","    plt.xlim([0.0, 1.0])\n","    plt.ylim([0.0, 1.05])\n","    plt.xlabel('False Positive Rate')\n","    plt.ylabel('True Positive Rate')\n","    plt.title(title)\n","    plt.legend(loc=\"lower right\")\n","    plt.show()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"I-BSHH6Z-JhD","colab_type":"code","colab":{}},"source":["def performance_evaluation(X_train, X_test, y_train, y_test, predicted_train, predicted_test, y_score, title=\"model\"):\n","    \n","    '''\n","    This function prints the results of the different classifiers,a s well as several performance metrics\n","    \n","    Args:\n","        X_train (ndarray): 2D array of input dataset used for training\n","        X_test (ndarray): 2D array of input dataset used for testing\n","        y_train (ndarray): 1D array of train labels \n","        y_test (ndarray): 1D array of test labels \n","        title (string): the classifier name\n","        predicted_train (ndarray): 1D array of model-predicted labels for the train dataset \n","        predicted_test (ndarray): 1D array of model-predicted labels for the test dataset\n","        \n","    Returns:\n","        ROC Plot\n","        \n","    '''\n","    \n","    print(\"For the \", title, \" classifier:\")\n","    print(\"Training set score: %f\" % accuracy_score(y_train,predicted_train ))\n","    print(\"Training log-loss: %f\" % log_loss(y_train, predicted_train))\n","    print(\"Training set confusion matrix:\")\n","    print(confusion_matrix(y_train,predicted_train))\n","    print(\"Training set classification report:\")\n","    print(classification_report(y_train,predicted_train))\n","    \n","    print(\"Test set score: %f\" % accuracy_score(y_test, predicted_test))\n","    print(\"Test log-loss: %f\" % log_loss(y_test, predicted_test))\n","    print(\"Test set confusion matrix:\")\n","    print(confusion_matrix(y_test,predicted_test))\n","    print(\"Test set classification report:\")\n","    print(classification_report(y_test,predicted_test))\n","\n","    ROC_plotting(\"ROC for \"+ title,y_test, y_score)\n","    fpr, tpr, _ = roc_curve(y_test, y_score)\n","    roc_auc = auc(fpr, tpr)\n","    print(\"AUC:\" + str(roc_auc))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"NQ1MXpKABvnu","colab_type":"code","colab":{}},"source":["def model_evaluation(function_name, X_train, X_test, y_train, y_test, title):\n","\n","  '''\n","  This function evaluates the propoesed model the results of the different classifiers,a s well as several performance metrics\n","\n","  Args:\n","      function_name (function): the function describing the employed model\n","      X_train (ndarray): 2D array of input dataset used for training\n","      X_test (ndarray): 2D array of input dataset used for testing\n","      y_train (ndarray): 1D array of train labels \n","      y_test (ndarray): 1D array of test labels \n","      title (string): the classifier name\n","      \n","  Returns:\n","      ROC Plot\n","      \n","  '''\n","\n","  if title == 'Neural Network':\n","    y_predicted_train, y_predicted_test, y_score = neural_network(X_train, X_test, y_train, y_test)\n","  else:\n","    y_predicted_train, y_predicted_test, y_score = function_name(X_train, y_train, X_test)\n","\n","  performance_evaluation(X_train, X_test, y_train, y_test, y_predicted_train, y_predicted_test, y_score, title)\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"dwXNx6ZXEQmZ","colab_type":"code","colab":{}},"source":["def multiple_model_evaluation(function_name, X, y, title):\n","\n","  '''\n","  This function takes the proposed model and original datasets and evaluates the proposed model by splitting the datasets randomly for 5 times.\n","  \n","  Args:\n","      function_name (function): the function describing the employed model\n","      X (ndarray): A 2D array containing the input processed data\n","      y (ndarray): A 1D array containing a list of labels, with 1 corresponding to \"active\" and 0 corresponding to \"dummy\"\n","      title (string): the classifier name\n","\n","  '''\n","\n","  random_states = [1, 10, 25, 42, 56]\n","\n","  test_set_scores = []\n","  test_log_losses = []\n","  roc_aucs = []\n","\n","  for random_state in random_states:\n","    X_train, X_test, y_train, y_test = data_split(X,y,random_state=random_state)\n","\n","    if title == 'Neural Network':\n","      y_predicted_train, y_predicted_test, y_score = neural_network(X_train, X_test, y_train, y_test)\n","    else:\n","      y_predicted_train, y_predicted_test, y_score = function_name(X_train, y_train, X_test)\n","\n","    test_set_score = accuracy_score(y_test, y_predicted_test)\n","    test_log_loss = log_loss(y_test, y_predicted_test)\n","\n","    fpr, tpr, _ = roc_curve(y_test, y_score)\n","    roc_auc = auc(fpr, tpr)\n","\n","    test_set_scores.append(test_set_score)\n","    test_log_losses.append(test_log_loss)\n","    roc_aucs.append(roc_auc)\n","\n","  print(\"The average test set score for \", title, \"is: \", str(np.mean(test_set_scores)))\n","  print(\"The average test log-loss for \", title, \"is: \", str(np.mean(test_log_loss)))\n","  print(\"The average AUC for \", title, \"is: \", str(np.mean(roc_auc)))\n","    \n"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"3DutKz7M-JhE","colab_type":"text"},"source":["## 4. Logistic regression, linear and quadratic discriminant analysis"]},{"cell_type":"markdown","metadata":{"id":"poYMdDSg-JhF","colab_type":"text"},"source":["### 4.1. Logistic regression"]},{"cell_type":"markdown","metadata":{"id":"h-41bPKh-JhF","colab_type":"text"},"source":["Logistic regression (logit regression, log-liner classifier) is a generalized linear model used for classification that uses a log-linear link function to model the outcome of a binary reponse variable $\\mathbf{y}$ using a single or multiple predictors $\\mathbf{X}$. Mathematically, the logistic regression primarily computes the probability of the value of a response variable given a value of the predictor, and this probability is then used for predicting the most probable outcome. The logistic regression has several advantages: it is easy to implement, it is efficient to train and it does not require input features to be scaled. However, the logistic regression can only produce a non-linear decision boundary. Therefore, with a complex dataset as ours, we do not expect it to perform particularly well."]},{"cell_type":"code","metadata":{"id":"GEYY_Mnn-JhG","colab_type":"code","colab":{}},"source":["def LogReg(X_train, y_train, X_test):\n","    \"\"\"Classification using logistic regression \n","\n","    Args:\n","        X_train: Predictor or feature values used for training\n","        y_train: Response values used for training\n","        X_test: Predictor or feature values used for predicting the response values using the classifier\n","\n","    Returns:\n","        y_predicted: The predicted response values\n","\n","    \"\"\"\n","    scaler = StandardScaler()\n","    scaler.fit(X_train)\n","    X_train = scaler.transform(X_train)\n","    X_test = scaler.transform(X_test)\n","    \n","    #Define and train the model\n","    classifier = LogisticRegression(max_iter = 500).fit(X_train, y_train)\n","    \n","    #Predict the response values using the test predictor data\n","    y_predicted_test = classifier.predict(X_test)\n","    y_predicted_train = classifier.predict(X_train)\n","    y_score = classifier.predict_proba(X_test)[:,1]\n","    return y_predicted_train, y_predicted_test, y_score"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ZG7ojPX6CXW_","colab_type":"code","outputId":"011ec509-1084-4b89-a679-62fdc15cb5d5","executionInfo":{"status":"ok","timestamp":1578578486291,"user_tz":0,"elapsed":28856,"user":{"displayName":"Andrei Roibu","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDUCsIj5qwCoMqexSria42B1d9boiojvcfQgzmzXA=s64","userId":"06542274079142548932"}},"colab":{"base_uri":"https://localhost:8080/","height":839}},"source":["model_evaluation(LogReg, X_train, X_test, y_train, y_test, title='Logistic Regression')"],"execution_count":12,"outputs":[{"output_type":"stream","text":["For the  Logistic Regression  classifier:\n","Training set score: 0.999351\n","Training log-loss: 0.022408\n","Training set confusion matrix:\n","[[41135     5]\n"," [   22   455]]\n","Training set classification report:\n","              precision    recall  f1-score   support\n","\n","         0.0       1.00      1.00      1.00     41140\n","         1.0       0.99      0.95      0.97       477\n","\n","    accuracy                           1.00     41617\n","   macro avg       0.99      0.98      0.99     41617\n","weighted avg       1.00      1.00      1.00     41617\n","\n","Test set score: 0.998270\n","Test log-loss: 0.059750\n","Test set confusion matrix:\n","[[10285     5]\n"," [   13   102]]\n","Test set classification report:\n","              precision    recall  f1-score   support\n","\n","         0.0       1.00      1.00      1.00     10290\n","         1.0       0.95      0.89      0.92       115\n","\n","    accuracy                           1.00     10405\n","   macro avg       0.98      0.94      0.96     10405\n","weighted avg       1.00      1.00      1.00     10405\n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nji5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44k+MGvULCQnJXHJJKM88cw2FC9vYTKbg8SRRBKUkCQBV3SAiNu2WybfmzNnKo4/O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\n0+Rf69ev93UIWrRo0dT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LM/fEf911j+MkrIUistBd115EFrvX8XMRKZbJeUwBZ4nC5EVF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\nGyPxoUcyUccAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["AUC:0.9931372797566231\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"PmZCIo9WLRvB","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":68},"outputId":"b714e923-543a-4a55-bdf1-ac4dd1cb36c4","executionInfo":{"status":"ok","timestamp":1578578505297,"user_tz":0,"elapsed":47858,"user":{"displayName":"Andrei Roibu","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDUCsIj5qwCoMqexSria42B1d9boiojvcfQgzmzXA=s64","userId":"06542274079142548932"}}},"source":["multiple_model_evaluation(LogReg, X, y, title='Logistic Regression')"],"execution_count":13,"outputs":[{"output_type":"stream","text":["The average test set score for  Logistic Regression is:  0.9984238346948583\n","The average test log-loss for  Logistic Regression is:  0.0531115059974207\n","The average AUC for  Logistic Regression is:  0.9981679131279841\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"uVI1esnn-JhL","colab_type":"text"},"source":["### 4.2. Linear discriminant analysis"]},{"cell_type":"markdown","metadata":{"id":"iUg0UvDU-JhL","colab_type":"text"},"source":["LDA employs Bayes' theorem to fit a Gaussian density to each class of data. The classes are assumed to have the same covariance matrix. This generates a linear decision boundry. "]},{"cell_type":"code","metadata":{"id":"DMLrSWIv-JhM","colab_type":"code","colab":{}},"source":["def LDA(X_train, y_train, X_test):\n","    \n","    \"\"\"Classification using LDA \n","\n","    Args:\n","        X_train: Predictor or feature values used for training\n","        y_train: Response values used for training\n","        X_test: Predictor or feature values used for predicting the response values using the classifier\n","\n","    Returns:\n","        y_predicted_train: The predicted response values for the training dataset\n","        y_predicted_test: The predicted response values for the test dataset\n","\n","    \"\"\"\n","    \n","    classifier = LinearDiscriminantAnalysis()\n","    classifier = classifier.fit(X_train, y_train)\n","    y_predicted_test = classifier.predict(X_test)\n","    y_predicted_train = classifier.predict(X_train)\n","    y_score = classifier.predict_proba(X_test)[:,1]\n","    return y_predicted_train, y_predicted_test, y_score"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"nYKkDczp-JhN","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":839},"outputId":"9cb272c9-a096-4689-b25e-c11e26ae441f","executionInfo":{"status":"ok","timestamp":1578578507455,"user_tz":0,"elapsed":50004,"user":{"displayName":"Andrei Roibu","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDUCsIj5qwCoMqexSria42B1d9boiojvcfQgzmzXA=s64","userId":"06542274079142548932"}}},"source":["model_evaluation(LDA, X_train, X_test, y_train, y_test, title='Linear Discriminant')"],"execution_count":15,"outputs":[{"output_type":"stream","text":["For the  Linear Discriminant  classifier:\n","Training set score: 0.995459\n","Training log-loss: 0.156857\n","Training set confusion matrix:\n","[[41046    94]\n"," [   95   382]]\n","Training set classification report:\n","              precision    recall  f1-score   support\n","\n","         0.0       1.00      1.00      1.00     41140\n","         1.0       0.80      0.80      0.80       477\n","\n","    accuracy                           1.00     41617\n","   macro avg       0.90      0.90      0.90     41617\n","weighted avg       1.00      1.00      1.00     41617\n","\n","Test set score: 0.995963\n","Test log-loss: 0.139418\n","Test set confusion matrix:\n","[[10266    24]\n"," [   18    97]]\n","Test set classification report:\n","              precision    recall  f1-score   support\n","\n","         0.0       1.00      1.00      1.00     10290\n","         1.0       0.80      0.84      0.82       115\n","\n","    accuracy                           1.00     10405\n","   macro avg       0.90      0.92      0.91     10405\n","weighted avg       1.00      1.00      1.00     10405\n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxPr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yL5zpO/oAS0XkwRzGZ0y+c/JkHE88sYjmzd9h7doD/PvfG4mPT/R2WMbkS5l1PfUB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\nLFEYY4zJ1P8DVfeZQvyb3IIAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["AUC:0.9932015042041661\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"i786lOi0-JhP","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":68},"outputId":"2749d774-7ff4-40be-f4c6-7fc25ed13d16","executionInfo":{"status":"ok","timestamp":1578578517373,"user_tz":0,"elapsed":59908,"user":{"displayName":"Andrei Roibu","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDUCsIj5qwCoMqexSria42B1d9boiojvcfQgzmzXA=s64","userId":"06542274079142548932"}}},"source":["multiple_model_evaluation(LDA, X, y, title='Linear Discriminant')"],"execution_count":16,"outputs":[{"output_type":"stream","text":["The average test set score for  Linear Discriminant is:  0.9950985103315715\n","The average test log-loss for  Linear Discriminant is:  0.12282021353011703\n","The average AUC for  Linear Discriminant is:  0.9908209743524741\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"tOVlSse4-JhQ","colab_type":"text"},"source":["### 4.3. Quadratic discriminant analysis"]},{"cell_type":"markdown","metadata":{"id":"4YvQ4cN1-JhQ","colab_type":"text"},"source":["QDA is similar to LDA, however it employs a quadratic decision boundary, rather than a linear one."]},{"cell_type":"code","metadata":{"id":"Z1NQDR-y-JhR","colab_type":"code","colab":{}},"source":["def QDA(X_train, y_train, X_test):\n","    \"\"\"Classification using QDA \n","\n","    Args:\n","        X_train: Predictor or feature values used for training\n","        y_train: Response values used for training\n","        X_test: Predictor or feature values used for predicting the response values using the classifier\n","\n","    Returns:\n","        y_predicted_train: The predicted response values for the training dataset\n","        y_predicted_test: The predicted response values for the test dataset\n","\n","    \"\"\"\n","    classifier = QuadraticDiscriminantAnalysis()\n","    classifier = classifier.fit(X_train, y_train)\n","    y_predicted_test = classifier.predict(X_test)\n","    y_predicted_train = classifier.predict(X_train)\n","    y_score = classifier.predict_proba(X_test)[:,1]\n","    return y_predicted_train, y_predicted_test, y_score"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"NM3FHUG5-JhS","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":839},"outputId":"cbe613dc-5054-468c-b2d6-f98ffb1b126b","executionInfo":{"status":"ok","timestamp":1578578519673,"user_tz":0,"elapsed":62173,"user":{"displayName":"Andrei Roibu","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDUCsIj5qwCoMqexSria42B1d9boiojvcfQgzmzXA=s64","userId":"06542274079142548932"}}},"source":["model_evaluation(QDA, X_train, X_test, y_train, y_test, title='Quadratic Discriminant')"],"execution_count":18,"outputs":[{"output_type":"stream","text":["For the  Quadratic Discriminant  classifier:\n","Training set score: 0.011798\n","Training log-loss: 34.132076\n","Training set confusion matrix:\n","[[   14 41126]\n"," [    0   477]]\n","Training set classification report:\n","              precision    recall  f1-score   support\n","\n","         0.0       1.00      0.00      0.00     41140\n","         1.0       0.01      1.00      0.02       477\n","\n","    accuracy                           0.01     41617\n","   macro avg       0.51      0.50      0.01     41617\n","weighted avg       0.99      0.01      0.00     41617\n","\n","Test set score: 0.011148\n","Test log-loss: 34.154512\n","Test set confusion matrix:\n","[[    1 10289]\n"," [    0   115]]\n","Test set classification report:\n","              precision    recall  f1-score   support\n","\n","         0.0       1.00      0.00      0.00     10290\n","         1.0       0.01      1.00      0.02       115\n","\n","    accuracy                           0.01     10405\n","   macro avg       0.51      0.50      0.01     10405\n","weighted avg       0.99      0.01      0.00     10405\n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nO3deZxN9R/H8dfHmAUzZsYSsmTNvtWk\npKTFkvpZ2oiSLdkiRAsiqWRPdiVt0kpatJCSIntkCUnMpCwxZjDM8vn9cS7GNGYu5s6Z5fN8PObh\nnnvP8r7HzP3c7/ec8z2iqhhjjDHnk8ftAMYYY7I2KxTGGGPSZIXCGGNMmqxQGGOMSZMVCmOMMWmy\nQmGMMSZNViiMMcakyQqFyVAi0kBEdohIrIi0cjvPxRCR3SJyWwau70YR+S2j1pcZ6/b8/5W/yGU3\ni0ijDI5kXGSFIofyfNid8PzB/y0ic0QkOMU814vItyISIyLRIvKpiFRLMU9BEZkoIns86/rdM13k\nPJseAUxW1WBVXZBB7yVlzoUiUiUj1u0LIqIiUvH0tKr+oKqVL2I9w0Uk3vO+Y0Rku4hMFpESl7ru\n9Hj+/3Zd5LLVVfW7DI70HyLynYh09fV2jBWKnO5/qhoM1AHqAk+dfkFE6gNfA58AlwPlgF+AH09/\nkxSRAGAJUB1oBhQE6gOHgHrn2eYVwOaLCSsieVN5LrWcGz05y17Mdi5Fahl97D1VDQEKAa2B4sDa\n5MUiI7nw/kx2oKr2kwN/gN3AbcmmRwOfJ5v+AZiaynKLgDc9j7sC/wDBXm7zdyAJOAHEAoE4H+4L\ngX+BncDDyeYfDnwIvA0cBbqmss60cr7uedwRWJ7idQUqeh7fAaz3bGMvMDzFvA8Cf+IUwMHJ911q\nGXGK5ArgCLAPmAwEeOZf5tn2Mc8+aAM0AiKTba808DFwwLPNyefZn8OBt1M854dT0Md6plOu+wkg\nCogBfgNuTbbc057/oxhgLVA62b7qBewA/khl/80Bpnr2eSzwI07BmggcBrYBdVP73fO8h/eBNz3b\n3QxEJJv3yWSZtgCtk73WEVgOjPVs5w/gds9rzwOJQJwnU6r70H4y5sdaFLmAiJQCbsf5oEZE8gPX\nAx+kMvv7QGPP49uAL1U11pvtqGoFYA+eloyqngTmAZE4BeMe4AURuSXZYi1xPojDgHdS5E4vZxNv\ncuF8aHfwbOMOoMfp4yeerrZpOMXicqAwUCrF8ikzJgL9gCI4LaxbgZ6efdDQs0xtzz54L8V78gM+\nwylMZYGSOPvIK6qaiNO6ujHlayJSGegNXKNOK6Qpzoc2QH/gfqA5TsuwM3A82eKtgGuBc7oek7kP\nGILznk/iFMp1nukPgfFpxG6B8x7DcL40TE722u+e9xIKPAu8naK1dC1OwSuC82XnNRERVR2M8yWi\nt2c/905j++YSWaHI2RaISAzOt+j9wDDP84Vw/u/3pbLMPpw/SnA+NFObxysiUhpoADyhqnGqugF4\nFedD+7QVqrpAVZNU9USKVaSXs6g3OVT1O1Xd5NnGRuBd4CbPy/cAn6nqMk9hG4rTKkrunIyqulZV\nV6pqgqruBmYkW1966uEUpIGqesyzX5Z7uexpf+Hsm5QScVpx1UTEX1V3q+rvnte6AkNU9Td1/KKq\nh5It+6Kq/pvK/8Fp8z3vOw6YD8Sp6puewvUeTtfm+SxX1S88874F1D79gqp+oKp/efbtezitmuTd\nmn+q6izPsm8AJYBiaWzL+IAVipytleebZSOgCmcLwGGcD8PU+rlLAAc9jw+dZx5vXQ78q6oxyZ77\nE+db9Gl701je25xpEpFrRWSpiBwQkWigO2f3xeXJM6jqMZz3ndw5GUXkShH5zHOSwFHghWTrS09p\nnA+/BC/nT01JnK68c6jqTuAxnO6e/SIyT0QuT7bd31Muk0xa/w/gdEGediKV6WDO7+9kj48DQaeP\nhYhIBxHZICJHROQIUINz9+WZZVX1dAsorW0ZH7BCkQuo6vc4/cxjPdPHcLoO7k1l9vtwDmADLAaa\nikiBi9z0X0AhEQlJ9lwZnD70M/HSyJ1ezu88j48B+U+/ICLFU8w7F6fLo7SqhgLTAfG8tg/nQ/T0\nsvlxWlLnREkxPQ2nX76SqhbE6fsXvLMXKHOxB41FJA/wP5xul/9Q1bmqegPOSQUKvJRsuxXSWHWm\n329ARK4AZuF0lxVW1TDgV7zfl3aPhExihSL3mAg0FpHTzf4ngYdEpI+IhIhIuIiMxOlzf9Yzz1s4\nHzAfiUgVEckjIoVF5GkRaZ7eBlV1L/AT8KKIBIlILaALzoFhb50v54043+TBObhbXUTqiEgQzjfq\n5EJwWjZxIlIPaJfstQ+BO0XkBs9ZXiNI/+8iBOfAdqznNN0eKV7/BzjfNQircIrTKBEp4NkvDdLZ\nHiKSV0Sq4nSbFSeVYwIiUllEbhGRQJyDvCc42432KvCciFQSRy0RSVkQM1sBnA/7AwAi0gmnReGt\ntPazyUBWKHIJVT2Ac+bJM57p5TgHO+/C+eD6E6ef+QZV3eGZ5yTOAe1twDc4H46rcLoGfvZy0/fj\nHLT9C6dve5iqLr6A3Clz/gs8hHM2z6+eebbjfMAvxunjTtnn3xMY4Tle8wzOgfDT69+Mc8bPXM/6\nD+McfE/L4zjFJgbnG/F7KV4fDrzh6U65L8X7ScRpEVTEOfAfiXNm1Pm0EZFYIBqnVXQIuFpV/0pl\n3kBgFE6X3N/AZZw9JXo8zvv+Guf/8TUgXzrv06dUdQswDqfV+A9QE+eMKm+9DNwjIodFZJIPIhoP\nUbXWm8k+PK2SpUA7Vf3K7TzG5AbWojDZiuespVZATbs4zJjMYS0KY4wxabIWhTHGmDRlu6Z7kSJF\ntGzZsm7HMMaYbGXt2rUHVdWri1RTynaFomzZsqxZs8btGMYYk62IyJ8Xu6x1PRljjEmTFQpjjDFp\nskJhjDEmTVYojDHGpMkKhTHGmDRZoTDGGJMmnxUKEZktIvtF5NfzvC4iMklEdorIRhG5yldZjDHG\nXDxftijmAM3SeP12oJLnpxvOGP/GGGMy2Km/Nl7S8j674E5Vl4lI2TRmaQm8qc5gUytFJExESqjq\nRd960xhjTDLH9zPwwRdYv+nwJa3GzWMUJTn39ouRnHuLzDNEpJuIrBGRNQcOHMiUcMYYk21pEmyc\nCbMrU8P/O37YVeaSVpctDmar6kxVjVDViKJFL2qoEmOMyRW2LF/O273uhW8egZNH6HB3MX5bd88l\nrdPNsZ6iSHavYqAU595L2RhjjJeORx9hZK8xjHnXD7881bhu6K9UbPMccuW9lBVvb0OeOjcLxUKg\nt4jMA64Fou34hDHGXLhFr71Fryd/4Y+DIQB0aX6Cwt2WQfFiGbJ+nxUKEXkXaAQUEZFIYBjgD6Cq\n04EvgObATuA40MlXWYwxJieK2raVxzrP4MMV4UAItUpHM33q7dS/s3GGbseXZz3dn87rinNTe2OM\nMRciMR7WT6JX5/V8sqkS+QPiGdGzIH1fepq8Af4Zvrlsdz8KY4zJzRL2/ETe73rAgY28dHth/EMu\nY9ysRyhTrbLPtmmFwhhjsoHof/5myCPj2b51H18+vBEJLUvl1lP44KXmPt+2FQpjjMnCNCmJDybO\n5LERu9gXXQC/POXZEP40dTsMBv/8mZLBCoUxxmRRv69dS++ub/LlhkJAAepXOsL0WfdQ66YGmZrD\nCoUxxmQ1CXGMfWwUQ2cmEBdfiLB8cbw08HK6PjOUPH6Zf520FQpjjMlK/lwMS3pyfMflxMXfzIO3\nxjL21Ue5rOylDcNxKaxQGGNMFnBg9x/89v5IbvCbDcATdwfQ6OG6NLynhcvJrFAYY4yrkhITmf3s\nywwad4C8UphtT4dR6NZBBEYMoKFfgNvxACsUxhjjml+/X0b3bh/x4/ZCQBCNa0dzvOUyClWv6Xa0\nc1ihMMaYTHbs8GFG9BzD+PfzkpBUiGIFjzNx+JW06TsUyZP1BvW2QmGMMZlFFXZ+wj2tP+PLzaUR\nUXq2jOP5GQMIK3aZ2+nOywqFMcZkhqN/wpJHYdenPHFjWf6Jbc20ac259vbb3E6WLisUxhjjQwkn\nT/LKk+PYvW4FL7f4DAJCaNRtAGte6U6evNnjIzh7pDTGmGxo1Wdf8Eivr9mwJxyIoNt94VR/aBQE\nX549bi/qkZ2yGmNMtnBk3z56/m8g17VYxYY94VxROJZPZ11J9V5vQvDlbse7YNaiMMaYjKLKvHHT\neWzEbv6JCSZvnkQGtBOGTh5CgbBQt9NdNCsUxhiTEQ5tgyU9+Pr9UP6JqUuDykeYNus+at5Y3+1k\nl8wKhTHGXIKTsTFEfTGa8lEvQVI8o+8pxY0tGvPQU+4M4OcLViiMMeYifTv3A3r0X0EePcUv/ZMI\nqNuVIjeOolO+wm5Hy1BWKIwx5gL9s+t3Hu8yhbe/CwVCqVLiKJE3LKJ8g8ZuR/MJKxTGGOOlpIQE\nZg2byJMTDnHkRChB/vEM6ZKPgeOfJyBfkNvxfMYKhTHGeOOfdbRuPouF64oDQTStE82U1ztSoU4t\nt5P5XM440mKMMb5yKgaW9oN3ruGuSisoXvA4700sw6K1Y3NFkQBrURhjTOpUWTh9DpHL5tIzYjFI\nHjo8cgt3TRlESOGcdbA6PVYojDEmhT2/bqJP59f4ZHU4gXmvo1m9eMq3m4AUq0uI2+FcYIXCGGM8\n4uNOMGnQOIbNOMGxU+GEBJ1k5KOFuaL3N+Dv73Y811ihMMYYYOXCz3ik9xI27g0DArj3hqNMmN2T\nkpU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D5Ym752eCylztdjRjMow37eEkzz2t7wJeUdV+QEnfxjIma4uPO8FLvUZSLeJ9Plsb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\njDHGpMkKhTHGmDRZoTDGGJOm/wNyMTnzeqfqKgAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["AUC:0.5000485908649174\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"8KOtdWIq-JhT","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":68},"outputId":"5763738a-1930-4006-c8f1-7bf2bb044fe8","executionInfo":{"status":"ok","timestamp":1578578528953,"user_tz":0,"elapsed":71419,"user":{"displayName":"Andrei Roibu","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDUCsIj5qwCoMqexSria42B1d9boiojvcfQgzmzXA=s64","userId":"06542274079142548932"}}},"source":["multiple_model_evaluation(QDA, X, y, title='Quadratic Discriminant')"],"execution_count":19,"outputs":[{"output_type":"stream","text":["The average test set score for  Quadratic Discriminant is:  0.011513695338779432\n","The average test log-loss for  Quadratic Discriminant is:  34.14787296811251\n","The average AUC for  Quadratic Discriminant is:  0.5001457725947522\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"OJsEHUWT-JhV","colab_type":"text"},"source":["## 5. Decision trees and random forest"]},{"cell_type":"markdown","metadata":{"id":"97FKNGjL-JhV","colab_type":"text"},"source":["### 5.1. Single decision tree"]},{"cell_type":"markdown","metadata":{"id":"9YukoH_0-JhW","colab_type":"text"},"source":["Decision trees are a non-parametric learning method used for both classification and regression. The advantages of decision trees are that they are easy to understand and they can be used for a broad range of data. However, the main disadvantages are that a single decision tree is easily overfitted and hence even small perturbations in the data might result in a markedly different classifier. This problem is tackled by generating several decision trees for deriving the final classifier. Here, we first train a single decision tree before we looking into more sophisticated ensemble methods."]},{"cell_type":"markdown","metadata":{"id":"cTVDocJr-JhW","colab_type":"text"},"source":["We fit a single decision tree with default parameters and predict the values of $\\mathbf{y}$ based on the test data."]},{"cell_type":"code","metadata":{"id":"6ou6VI3--JhW","colab_type":"code","colab":{}},"source":["def DecisionTree(X_train, y_train, X_test):\n","    \n","    \"\"\"Classification using Decision Tree \n","\n","    Args:\n","        X_train: Predictor or feature values used for training\n","        y_train: Response values used for training\n","        X_test: Predictor or feature values used for predicting the response values using the classifier\n","\n","    Returns:\n","        y_predicted_train: The predicted response values for the training dataset\n","        y_predicted_test: The predicted response values for the test dataset\n","\n","    \"\"\"\n","    \n","    classifier = tree.DecisionTreeClassifier()\n","    classifier = classifier.fit(X_train, y_train)\n","    y_predicted_test = classifier.predict(X_test)\n","    y_predicted_train = classifier.predict(X_train)\n","    y_score = classifier.predict_proba(X_test)[:,1]\n","    return y_predicted_train, y_predicted_test, y_score"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"colab_type":"code","id":"CwVFfKqbR3XD","colab":{"base_uri":"https://localhost:8080/","height":839},"outputId":"3139267b-5b8f-4c69-fe9d-16a13c02a15f","executionInfo":{"status":"ok","timestamp":1578578534895,"user_tz":0,"elapsed":77338,"user":{"displayName":"Andrei Roibu","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDUCsIj5qwCoMqexSria42B1d9boiojvcfQgzmzXA=s64","userId":"06542274079142548932"}}},"source":["model_evaluation(DecisionTree, X_train, X_test, y_train, y_test, title='Decision Tree')"],"execution_count":21,"outputs":[{"output_type":"stream","text":["For the  Decision Tree  classifier:\n","Training set score: 1.000000\n","Training log-loss: 0.000000\n","Training set confusion matrix:\n","[[41140     0]\n"," [    0   477]]\n","Training set classification report:\n","              precision    recall  f1-score   support\n","\n","         0.0       1.00      1.00      1.00     41140\n","         1.0       1.00      1.00      1.00       477\n","\n","    accuracy                           1.00     41617\n","   macro avg       1.00      1.00      1.00     41617\n","weighted avg       1.00      1.00      1.00     41617\n","\n","Test set score: 0.997117\n","Test log-loss: 0.099584\n","Test set confusion matrix:\n","[[10274    16]\n"," [   14   101]]\n","Test set classification report:\n","              precision    recall  f1-score   support\n","\n","         0.0       1.00      1.00      1.00     10290\n","         1.0       0.86      0.88      0.87       115\n","\n","    accuracy                           1.00     10405\n","   macro avg       0.93      0.94      0.93     10405\n","weighted avg       1.00      1.00      1.00     10405\n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1Uh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W7O/wjCkUT6Wnb1T1QhHZB+S8PVZwxvOL8Xl0+bHSkynlMjKUV1/9maSkVLp3b8yE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\nYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["AUC:0.9383529809439304\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"jb4lh4MG-Jhb","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":68},"outputId":"db581b19-881c-453b-ad8a-2f50f0b69eb4","executionInfo":{"status":"ok","timestamp":1578578559821,"user_tz":0,"elapsed":102247,"user":{"displayName":"Andrei Roibu","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDUCsIj5qwCoMqexSria42B1d9boiojvcfQgzmzXA=s64","userId":"06542274079142548932"}}},"source":["multiple_model_evaluation(DecisionTree, X, y, title='Decision Tree')"],"execution_count":22,"outputs":[{"output_type":"stream","text":["The average test set score for  Decision Tree is:  0.9968092263334934\n","The average test log-loss for  Decision Tree is:  0.10622347307937488\n","The average AUC for  Decision Tree is:  0.9382557992140955\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Noeyq5nR-Jhd","colab_type":"text"},"source":["### 5.2. Random forest"]},{"cell_type":"markdown","metadata":{"id":"0M2WtC7S-Jhe","colab_type":"text"},"source":["Radnom forest explanation..."]},{"cell_type":"code","metadata":{"id":"IZaPW3Wr-Jhf","colab_type":"code","colab":{}},"source":["def RandomForest(X_train, y_train, X_test):\n","    \n","    \"\"\"Classification using Random Forest \n","\n","    Args:\n","        X_train: Predictor or feature values used for training\n","        y_train: Response values used for training\n","        X_test: Predictor or feature values used for predicting the response values using the classifier\n","\n","    Returns:\n","        y_predicted_train: The predicted response values for the training dataset\n","        y_predicted_test: The predicted response values for the test dataset\n","\n","    \"\"\"\n","    \n","    rf_classifier = RandomForestClassifier(n_estimators=200)\n","    rf_classifier = rf_classifier.fit(X_train, y_train)\n","    y_predicted_test = rf_classifier.predict(X_test)\n","    y_predicted_train = rf_classifier.predict(X_train)\n","    y_score = rf_classifier.predict_proba(X_test)[:,1]\n","    return y_predicted_train, y_predicted_test, y_score"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"N54Jn1Vh-Jhh","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":839},"outputId":"8b2f320c-c1e4-4cec-b48b-a33688f644c2","executionInfo":{"status":"ok","timestamp":1578578603400,"user_tz":0,"elapsed":145791,"user":{"displayName":"Andrei Roibu","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDUCsIj5qwCoMqexSria42B1d9boiojvcfQgzmzXA=s64","userId":"06542274079142548932"}}},"source":["model_evaluation(RandomForest, X_train, X_test, y_train, y_test, title='Random Forest')"],"execution_count":24,"outputs":[{"output_type":"stream","text":["For the  Random Forest  classifier:\n","Training set score: 1.000000\n","Training log-loss: 0.000000\n","Training set confusion matrix:\n","[[41140     0]\n"," [    0   477]]\n","Training set classification report:\n","              precision    recall  f1-score   support\n","\n","         0.0       1.00      1.00      1.00     41140\n","         1.0       1.00      1.00      1.00       477\n","\n","    accuracy                           1.00     41617\n","   macro avg       1.00      1.00      1.00     41617\n","weighted avg       1.00      1.00      1.00     41617\n","\n","Test set score: 0.997790\n","Test log-loss: 0.076347\n","Test set confusion matrix:\n","[[10290     0]\n"," [   23    92]]\n","Test set classification report:\n","              precision    recall  f1-score   support\n","\n","         0.0       1.00      1.00      1.00     10290\n","         1.0       1.00      0.80      0.89       115\n","\n","    accuracy                           1.00     10405\n","   macro avg       1.00      0.90      0.94     10405\n","weighted avg       1.00      1.00      1.00     10405\n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLOX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b5xmKS0Skj6ouzckJjfEnGzfu5+GHv+L77/8kMjKc1av7ERQUQOnSIb4OzZg840nT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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["AUC:0.9934774158110449\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"zOTtJM4r-Jhi","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":68},"outputId":"520baaae-8165-4c22-f2f3-e3f0c4d8dd17","executionInfo":{"status":"ok","timestamp":1578578795350,"user_tz":0,"elapsed":337732,"user":{"displayName":"Andrei Roibu","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDUCsIj5qwCoMqexSria42B1d9boiojvcfQgzmzXA=s64","userId":"06542274079142548932"}}},"source":["multiple_model_evaluation(RandomForest, X, y, title='Random Forest')"],"execution_count":25,"outputs":[{"output_type":"stream","text":["The average test set score for  Random Forest is:  0.9977703027390676\n","The average test log-loss for  Random Forest is:  0.08298600767638417\n","The average AUC for  Random Forest is:  0.9997160603371784\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Cjpz-f_r-Jhk","colab_type":"text"},"source":["## 6. Neural Network"]},{"cell_type":"markdown","metadata":{"id":"QQ_tRmHK-Jhl","colab_type":"text"},"source":["A Neural Network, also known as a multi-layered perceptron, is a supervised learning algorithm that learns a function, which is trained using a set of features and targets. A neural network can learns a non-linear function approximator, allowing classification of data. Between the input and output layers, there are a set of non-linear hidden layers. The advantages of a neural network are it's ability to learn non-linear models and perform learning in real-time. However, a NN can suffer from different validation accuracy induced by random weight initialization, has a large number of hyper parameters which require tunning and is sensitive to feature scaling. \n","\n","The neural_network function below makes use of inbuilt MLPClassifier, which implements a multi-layer perceptron (MLP) algorithm that trains using Backpropagation.\n","\n","As MLPs are sensitive to feature scaling, the data is scaled using the built-in StandardScaler for standardization. The same scaling s applied to the test set for meaningful results.\n","\n","Most of the MLPClassifier's parameters where left to random. However, several were modifed in order to enhance performance. Firstly, the solver was set to _adam_, which reffers to a stochastic gradient-based optimizer, the alpha regularization parameter was set to 1e-5, the number of hidden layers was set to 2, each with 70 neurons (numbers determined through experimentation throughout the day), and the max_iterations was set to 1500."]},{"cell_type":"code","metadata":{"id":"rZxV_gfW-Jhl","colab_type":"code","colab":{}},"source":["def neural_network(X_train, X_test, y_train, y_test):\n","    \n","    '''\n","    This function takes in the input datasets, creates a neural network, trains and then tests it.\n","    \n","    Written by AndreiRoibu\n","    \n","    Args:\n","        X_train (ndarray): 2D array of input dataset used for training\n","        X_test (ndarray): 2D array of input dataset used for testing\n","        y_train (ndarray): 1D array of train labels \n","        y_test (ndarray): 1D array of test labels  \n","        \n","    Returns:\n","        predicted_train (ndarray): 1D array of model-predicted labels for the train dataset \n","        predicted_test (ndarray): 1D array of model-predicted labels for the test dataset\n","        \n","    '''\n","    \n","    scaler = StandardScaler()\n","    scaler.fit(X_train)\n","    X_train = scaler.transform(X_train)\n","    X_test = scaler.transform(X_test)\n","    \n","    classifier = MLPClassifier(solver='adam', alpha=1e-5, hidden_layer_sizes=(70,70), random_state=1, max_iter=1500)\n","    \n","    classifier.fit(X_train, y_train)\n","    \n","    predicted_train = classifier.predict(X_train)\n","    predicted_test = classifier.predict(X_test)\n","    y_score = classifier.predict_proba(X_test)[:,1]\n","    return predicted_train, predicted_test, y_score"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"SfzUy_XE-Jhn","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":839},"outputId":"a10dcf6e-f810-4015-fde8-1022d62c4116","executionInfo":{"status":"ok","timestamp":1578578806965,"user_tz":0,"elapsed":349325,"user":{"displayName":"Andrei Roibu","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDUCsIj5qwCoMqexSria42B1d9boiojvcfQgzmzXA=s64","userId":"06542274079142548932"}}},"source":["model_evaluation(neural_network, X_train, X_test, y_train, y_test, title='Neural Network')"],"execution_count":27,"outputs":[{"output_type":"stream","text":["For the  Neural Network  classifier:\n","Training set score: 1.000000\n","Training log-loss: 0.000000\n","Training set confusion matrix:\n","[[41140     0]\n"," [    0   477]]\n","Training set classification report:\n","              precision    recall  f1-score   support\n","\n","         0.0       1.00      1.00      1.00     41140\n","         1.0       1.00      1.00      1.00       477\n","\n","    accuracy                           1.00     41617\n","   macro avg       1.00      1.00      1.00     41617\n","weighted avg       1.00      1.00      1.00     41617\n","\n","Test set score: 0.998654\n","Test log-loss: 0.046472\n","Test set confusion matrix:\n","[[10287     3]\n"," [   11   104]]\n","Test set classification report:\n","              precision    recall  f1-score   support\n","\n","         0.0       1.00      1.00      1.00     10290\n","         1.0       0.97      0.90      0.94       115\n","\n","    accuracy                           1.00     10405\n","   macro avg       0.99      0.95      0.97     10405\n","weighted avg       1.00      1.00      1.00     10405\n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFAp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27UH+85+NxMcnejssY3Kl9JqeHgDqqepZESkFLAA+ypmwjPEMVWXWrC08//wiDh48\ng7+/0K9fY1577TZrZjImDeklijhVPQugqkdExMYFGp92+nQcXbvOYuHCXQDcdFM5IiI6Ur/+VV6O\nzJjcLb1EUTXFWtkCVEu5draq3uPRyIzJZoULFyAuLpFixYIYOfIOnnrqBvz8bApwYzKSXqLoctn2\nBE8GYownrFz5J2XLFqZGjTBEhI8+6kRwcABlyhT2dmjG+Iz01sxempOBGJOdjh49x0svLebjj9fT\nsmUVFi/ugYhQqVJxb4dmjM+xifNNnpKUpEybtp7+/Rdz/HgMBQr4c+utFUlMVAICrJnJmKzwaAe1\niLQVke0isktEBqZTrouIqIjY/FEmyzZvPkyLFtN4/PG5HD8eQ8uWVfj992cYOrQFAQE2FsOYrHK7\nRiEiQaoal4ny/sBEoBUQBawRkbkp541ylSsCPA/84u65jblcdHQsjRt/yJkz5ylduhBjxrTm/vvr\n2nrVxmSDDL9miUgjEfkd2Onavk5E3JnCoxHO2hW7VfU8MBPonEq54cBbQKz7YRvjuDCzQLFiwQwY\n0JSePW9g27ZneeCBepYkjMkm7tTHxwMdgWMAqroBuM2N48oB+1NsR3HZWtsicj1QQVXnp3ciEXlK\nRCJFJNKN65p84MCBU9x77xfMmLExed/gwbcyeXJHSpSwWV6NyU7uJAo/Vf3zsn1XPNeB6wG+McCL\nGZVV1amq2jCr85SYvCMhIYlx41ZTq9ZEvvpqK0OHLicxMQnAahDGeIg7fRT7RaQRoK5+h+eAHW4c\ndwCokGK7vGvfBUWAOsBy1z/wq4C5ItJJVa3mYP5hzZoD9Ow5n3XrDgJw1121GD++Lf7+1lFtjCe5\nkyiewWl+qggcApa49mVkDVBDRKrgJIhuwP0XXlTVaKDkhW0RWQ78nyUJc7mzZ88zYMASJk1agypU\nrFiM995rR6dONb0dmjH5QoaJQlUP43zIZ4qqJohIb+A7wB/4SFU3i8gwIFJV52Y6WpMvBQT4sWTJ\nbvz8hH79mjB0aHMKFbJFFo3JKemuRwEgIu8D/yikqk95Kqj02HoU+cMffxynePFgwsJCAKfZKTg4\ngLp1y3g5MmN8k0fWo0hhCbDU9fMTUBpw+3kKYzIjLi6BESNWUqfOZAYMWJK8/8Yby1mSMMZL3Gl6\n+jzltoj8B/jRYxGZfGv58r0888x8tm07CjgjnBITk6yz2hgvy8pcT1UA+2pnss3hw2fp338x06dv\nAKBmzTAmT+7AbbdV8XJkxhhwI1GIyAku9lH4AceBNOdtMiYzjh49R3j4RI4fjyEoyJ/Bg2/lpZea\nEhRk81Uak1uk+69RnAccruPi8w9JmlHvtzGZULJkCJ071yQq6hSTJnWgevVQb4dkjLmMO6OeNqlq\nnRyKJ0M26sm3nT17nmHDVgmTsPgAABmlSURBVNChwzU0a1YJgNjYBIKC/O3JamM8yNOjntaLSIOs\nnNyYlL79dju1a09i1KhV9Oo1n6QkJ+EHBwdYkjAmF0uz6UlEAlQ1AWiAM0X4H8BZnPWzVVWvz6EY\njY/bvz+a559fxOzZ2wBo0OAqpkzpaOtVG+Mj0uuj+BW4HuiUQ7GYPCYhIYnx43/hlVeWcfZsPIUL\nF2DEiNt49tlGtpCQMT4kvUQhAKr6Rw7FYvKYU6fiePPNHzl7Np4uXcJ59922lC9f1NthGWMyKb1E\nUUpE+qX1oqqO8UA8xsedPBlLwYIBBAUFEBpakClTOhIU5E+HDtd4OzRjTBalV//3BwrjTAee2o8x\nyVSVTz/9nZo1JzBq1E/J+++5J9yShDE+Lr0axUFVHZZjkRiftWPHMXr1ms/SpXsAWLlyH6pqI5mM\nySMy7KMwJi2xsQm89daPvPHGj5w/n0hoaEHefrsVjzxS35KEMXlIeomiZY5FYXzO33+foVmzj9m5\n8zgAjzxSn7ffbkXJkiFejswYk93STBSqejwnAzG+pUyZQlSoUIyAAD8mT+5A8+aVvR2SMcZDbOY1\n45akJOX999dy221VuOaaMESETz+9hxIlClKggL+3wzPGeJA99WQytGHD3zRt+hE9e86nV6/5XJgf\nrEyZwpYkjMkHrEZh0nTmzHlefXU57767msRE5eqri9CzZ5bmFDPG+DBLFCZV33yzjeeeW0hU1Cn8\n/ITnnmvEiBG3U7RokLdDM8bkMEsU5h8OHDhFt26ziItL5IYbyhIR0ZGGDa/2dljGGC+xRGEAiI9P\nJCDADxGhXLmivP767RQo4E+vXjfamtXG5HP2CWBYtWo/N9wwlRkzNibve/HFm3nuuZssSRhjLFHk\nZ8ePx/D009/StOlH/P77YSZNisRWujXGXM6anvIhVWXGjI28+OL3HDlyjsBAP156qSmDB99qU28Y\nY/7BEkU+c+jQGbp3/4ply/YC0Lx5JSZP7kB4eCnvBmaMybUsUeQzxYsHc/DgGUqWDGH06FY89NB1\nVoswxqTLEkU+sHjxH1x/fVnCwkIICgrgyy/vo2zZwoSF2QR+xpiMWWd2Hnbw4Gm6d/+K1q1nMGDA\nkuT9deqUtiRhjHGb1SjyoMTEJKZMWcugQUs5dSqOggUDqFkzzBYTMsZkiSWKPGbduoP07DmPNWv+\nAqBDhxpMmNCeypWLezkyY4yvskSRh+zde5JGjd4nMVEpV64I48e34+67a1ktwhhzRTyaKESkLTAO\n8Ac+UNWRl73eD3gCSACOAI+p6p+ejCkvq1y5OI8+Wp8iRYJ47bUWFCliE/gZY66cxzqzRcQfmAi0\nA2oD3UWk9mXFfgMaqmo9YBYwylPx5EV7957kzjs/Y8WKvcn7pk69kzFj2liSMMZkG0/WKBoBu1R1\nN4CIzAQ6A1suFFDVZSnKrwYe9GA8eUZ8fCJjxvzMa6+tICYmgaNHz/Hzz48DWDOTMSbbeXJ4bDlg\nf4rtKNe+tDwOLEztBRF5SkQiRSQyG+PzST/+uI8GDaYwcOBSYmIS6NatDl9//S9vh2WMycNyRWe2\niDwINASap/a6qk4FpgI0rCD5cta6Eydi6N9/MR9++BsA1aqVYNKkDrRuXc3LkRlj8jpPJooDQIUU\n2+Vd+y4hIncAg4HmqhrnwXh8WlKSMmfOdgID/Rg48BYGDbqFggUDvR2WMSYf8GSiWAPUEJEqOAmi\nG3B/ygIi0gCYArRV1cMejMUnbdt2lCpVihMUFEBYWAj//e89VKxYjFq1Sno7NGNMPuKxPgpVTQB6\nA98BW4EvVHWziAwTkU6uYm8DhYEvRWS9iMz1VDy+5Ny5eAYPXkq9epMZNeqn5P2tW1ezJGGMyXEe\n7aNQ1QXAgsv2vZLi9zs8eX1ftGjRLnr1ms+ePScBOHr0nJcjMsbkd7miM9vAX3+d5oUXFvHll87o\n4bp1SxMR0ZGbb66QwZHGGONZlihygR07jtGw4VROnz5PSEggr77anBdeaExgoL+3QzPGGEsUuUGN\nGqHceGM5ChUK5L332lGpkk3gZ4zJPSxReMGpU3G88soyevW6kWuuCUNEmDu3G4UKFfB2aMYY8w+W\nKHKQqjJr1haef34RBw+eYdu2oyxa5MxaYknCGJNbWaLIIbt3n6B37wUsXLgLgMaNy/PWWzboyxiT\n+1mi8LDz5xMZPXoVw4evJDY2geLFgxk5siVPPnkDfn42gZ8xJvezROFh+/dHM2zYCuLiEnnggbq8\n805rypQp7O2wjDHGbZYoPODEiRiKFw9GRKhWLZRx49pSvXooLVtW9XZoxhiTaZ6cZjzfSUpSPvro\nN6pXf48ZMzYm73/66YaWJIwxPssSRTbZvPkwLVpM4/HH53L8eExyp7Uxxvg6a3q6QufOxTN8+ApG\nj/6ZhIQkSpcuxNixbejevY63QzPGmGxhieIK7NhxjDZtZrB370lEoGfPG3jjjZaUKFHQ26EZY0y2\nsURxBSpVKkZwcADXXVeGiIiONG5c3tshmVwkPj6eqKgoYmNjvR2KyUeCg4MpX748gYHZt7CZJYpM\nSEhIIiIiku7d6xAWFkJQUACLFj1AuXJFCQiw7h5zqaioKIoUKULlypURsWdmjOepKseOHSMqKooq\nVapk23nt081Nv/56gEaN3ue55xYyYMCS5P2VKhW3JGFSFRsbS1hYmCUJk2NEhLCwsGyvxVqNIgPR\n0bEMHvw/Jk1agypUrFiMzp1rejss4yMsSZic5om/OUsUaVBVPv98M337fsfff58hIMCPfv0a88or\nzW0CP2NMvmJtJmnYsOEQ3bt/xd9/n+Hmmyuwbt1TvPVWK0sSxqf4+/tTv3596tSpw5133snJkyeT\nX9u8eTO33347NWvWpEaNGgwfPhxVTX594cKFNGzYkNq1a9OgQQNefPFFb7yFdP322288/vjj3g4j\nTceOHeO2226jcOHC9O7dO81yx48fp1WrVtSoUYNWrVpx4sQJwPnC2qdPH6pXr069evVYt24dAEeO\nHKFt27Y58h7AEsUlEhOTkn+vX/8q+vZtzPvv38kPPzxK3bplvBiZMVlTsGBB1q9fz6ZNmwgNDWXi\nxIkAxMTE0KlTJwYOHMj27dvZsGEDq1atYtKkSQBs2rSJ3r17M2PGDLZs2UJkZCTVq1fP1tgSEhKu\n+BxvvPEGffr0ydFrZkZwcDDDhw9n9OjR6ZYbOXIkLVu2ZOfOnbRs2ZKRI0cCTrLeuXMnO3fuZOrU\nqTzzzDMAlCpVirJly/LTTz95/D2ANT0lW7ZsD716LWDKlI40a1YJgDFj2ng5KpNnvOOhvooXNeMy\nLk2aNGHjRmdqmU8//ZSmTZvSunVrAEJCQpgwYQItWrTg2WefZdSoUQwePJhatWoBTs3kwodUSmfO\nnOG5554jMjISEWHo0KF06dKFwoULc+bMGQBmzZrFvHnzmDZtGo888gjBwcH89ttvNG3alK+//pr1\n69dTvLizqmONGjX48ccf8fPzo2fPnuzbtw+Ad999l6ZNm15y7dOnT7Nx40auu+46AH799Veef/55\nYmNjKViwIB9//DE1a9Zk2rRpfP3115w5c4bExERWrFjB22+/zRdffEFcXBx33303r732GgB33XUX\n+/fvJzY2lueff56nnnrK7fubmkKFCnHLLbewa1f6MzXMmTOH5cuXA/Dwww/TokUL3nrrLebMmcND\nDz2EiNC4cWNOnjzJwYMHKVu2LHfddRf//e9//3FfPCHfJ4rDh8/Sv/9ipk/fAMCYMT8nJwpj8orE\nxESWLl2a3EyzefNmbrjhhkvKVKtWjTNnznDq1Ck2bdrkVlPT8OHDKVasGL///jtAcpNJeqKioli1\nahX+/v4kJiYye/ZsHn30UX755RcqVapEmTJluP/+++nbty+33HIL+/bto02bNmzduvWS80RGRlKn\nzsUZEGrVqsUPP/xAQEAAS5Ys4d///jdfffUVAOvWrWPjxo2Ehoby/fffs3PnTn799VdUlU6dOrFy\n5UqaNWvGRx99RGhoKDExMdx444106dKFsLCwS67bt29fli1b9o/31a1bNwYOHJjh+0/NoUOHKFu2\nLABXXXUVhw4dAuDAgQNUqFAhuVz58uU5cOAAZcuWpWHDhgwZMiRL18usfJsokpKUDz9cx4ABSzhx\nIpagIH+GDGlG//43ezs0kxdl4pt/doqJiaF+/focOHCA8PBwWrVqla3nX7JkCTNnzkzeLlGiRIbH\n3Hffffj7+wPQtWtXhg0bxqOPPsrMmTPp2rVr8nm3bNmSfMypU6c4c+YMhQtfnKL/4MGDlCpVKnk7\nOjqahx9+mJ07dyIixMfHJ7/WqlUrQkNDAfj+++/5/vvvadCgAeDUinbu3EmzZs0YP348s2fPBmD/\n/v3s3LnzH4li7Nix7t2cLBIRt0YulS5dmr/++sujsVyQLxPFnj0nePDB2axatR+A1q2rMXFie6pX\nD/VyZMZkrwt9FOfOnaNNmzZMnDiRPn36ULt2bVauXHlJ2d27d1O4cGGKFi3Ktddey9q1a5ObdTIr\n5Qfd5WP6CxUqlPx7kyZN2LVrF0eOHOGbb75J/oaclJTE6tWrCQ4OTve9pTz3yy+/zG233cbs2bPZ\nu3cvLVq0SPWaqsqgQYN4+umnLznf8uXLWbJkCT///DMhISG0aNEi1ecRPFGjKFOmTHKT0sGDByld\nujQA5cqVY//+/cnloqKiKFeuHEByE1tOyJed2UWLBrFjxzGuuqowM2d2YdGiByxJmDwtJCSE8ePH\n884775CQkMADDzzAjz/+yJIlzsOjMTEx9OnTh5deegmA/v3788Ybb7Bjxw7A+eCOiIj4x3lbtWqV\n3EEOF5ueypQpw9atW0lKSkr+hp4aEeHuu++mX79+hIeHJ397b926Ne+9915yufXr1//j2PDw8Eva\n/qOjo5M/RKdNm5bmNdu0acNHH32U3Idy4MABDh8+THR0NCVKlCAkJIRt27axevXqVI8fO3Ys69ev\n/8dPVpMEQKdOnfjkk08A+OSTT+jcuXPy/unTp6OqrF69mmLFiiU3Ue3YseOSpjdPyjeJ4rvvdhEX\n54x4CAsLYe7cbmzb9ixdu9axh6JMvtCgQQPq1avHZ599RsGCBZkzZw4jRoygZs2a1K1blxtvvDF5\nCGe9evV499136d69O+Hh4dSpU4fdu3f/45xDhgzhxIkT1KlTh+uuuy75m/bIkSPp2LEjN998c/IH\nW1q6du3KjBkzkpudAMaPH09kZCT16tWjdu3aqSapWrVqER0dzenTpwF46aWXGDRoEA0aNEh3dFPr\n1q25//77adKkCXXr1uXee+/l9OnTtG3bloSEBMLDwxk4cCCNGzfO+Ka6oXLlyvTr149p06ZRvnz5\n5Ca1J554gsjISAAGDhzI4sWLqVGjBkuWLElOOu3bt6dq1apUr16dJ598MnlUGsCyZcvo0KFDtsSY\nEUk5btoXNKwgGrnf/Zj374+mT59FfPPNNoYPv40hQ5p5MDpjLtq6dSvh4eHeDiNPGzt2LEWKFOGJ\nJ57wdig5rlmzZsyZMyfVfqHU/vZEZK2qNszKtfJsjSIhIYkxY34mPHwi33yzjcKFCxAaatN/G5OX\nPPPMMwQFBXk7jBx35MgR+vXr59bggeyQJzuzV6+OomfPeWzY4Awx69IlnHHj2lKuXFEvR2aMyU7B\nwcH06NHD22HkuFKlSnHXXXfl2PXyXKL45Zcobr75Q1ShcuXiTJjQjg4drvF2WCafUlXrAzM5yhPd\nCXkuUTRqVI42barToMFVDBnSjJCQ7Fu8w5jMCA4O5tixYzbVuMkxF9ajSG9YcVb4fGf2zp3H6Nv3\nO8aMacM11zhD65KSFD8/+4dpvMtWuDPekNYKd1fSme2zNYq4uARGjvyRN9/8kbi4RIKDA5g1618A\nliRMrhAYGJitq4wZ4y0eHfUkIm1FZLuI7BKRfzyNIiJBIvK56/VfRKSyO+ddunQ39epF8OqrK4iL\nS+TRR+sTEdExu8M3xhiDB2sUIuIPTARaAVHAGhGZq6pbUhR7HDihqtVFpBvwFtD1n2e7aM/x4txx\nx38ACA8vSURER5vEzxhjPMiTNYpGwC5V3a2q54GZQOfLynQGPnH9PgtoKRn0+p04V5Dg4ADeeON2\n1q/vaUnCGGM8zGOd2SJyL9BWVZ9wbfcAblLV3inKbHKViXJt/+Eqc/Sycz0FXJgYvg6wySNB+56S\nwNEMS+UPdi8usntxkd2Li2qqapGsHOgTndmqOhWYCiAikVntuc9r7F5cZPfiIrsXF9m9uEhEIrN6\nrCebng4AFVJsl3ftS7WMiAQAxYBjHozJGGNMJnkyUawBaohIFREpAHQD5l5WZi7wsOv3e4H/qa89\n2GGMMXmcx5qeVDVBRHoD3wH+wEequllEhgGRqjoX+BD4j4jsAo7jJJOMTPVUzD7I7sVFdi8usntx\nkd2Li7J8L3zuyWxjjDE5K89OM26MMSZ7WKIwxhiTrlybKDw1/YcvcuNe9BORLSKyUUSWikiefQox\no3uRolwXEVERybNDI925FyLyL9ffxmYR+TSnY8wpbvwbqSgiy0TkN9e/k/beiNPTROQjETnsekYt\ntddFRMa77tNGEbnerROraq77wen8/gOoChQANgC1LyvTC4hw/d4N+NzbcXvxXtwGhLh+fyY/3wtX\nuSLASmA10NDbcXvx76IG8BtQwrVd2ttxe/FeTAWecf1eG9jr7bg9dC+aAdcDm9J4vT2wEBCgMfCL\nO+fNrTUKj0z/4aMyvBequkxVz7k2V+M8s5IXufN3ATAcZ96wvDy/tzv34klgoqqeAFDVwzkcY05x\n514ocGGJy2LAXzkYX45R1ZU4I0jT0hmYro7VQHERKZvReXNroigH7E+xHeXal2oZVU0AooGwHIku\nZ7lzL1J6HOcbQ16U4b1wVaUrqOr8nAzMC9z5u7gGuEZEfhKR1SLSNseiy1nu3ItXgQdFJApYADyX\nM6HlOpn9PAF8ZAoP4x4ReRBoCDT3dizeICJ+wBjgES+HklsE4DQ/tcCpZa4UkbqqetKrUXlHd2Ca\nqr4jIk1wnt+qo6pJ3g7MF+TWGoVN/3GRO/cCEbkDGAx0UtW4HIotp2V0L4rgTBq5XET24rTBzs2j\nHdru/F1EAXNVNV5V9wA7cBJHXuPOvXgc+AJAVX8GgnEmDMxv3Po8uVxuTRQ2/cdFGd4LEWkATMFJ\nEnm1HRoyuBeqGq2qJVW1sqpWxumv6aSqWZ4MLRdz59/INzi1CUSkJE5T1O6cDDKHuHMv9gEtAUQk\nHCdRHMnRKHOHucBDrtFPjYFoVT2Y0UG5sulJPTf9h89x8168DRQGvnT15+9T1U5eC9pD3LwX+YKb\n9+I7oLWIbAESgf6qmudq3W7eixeB90WkL07H9iN58YuliHyG8+WgpKs/ZigQCKCqETj9M+2BXcA5\n4FG3zpsH75UxxphslFubnowxxuQSliiMMcakyxKFMcaYdFmiMMYYky5LFMYYY9JlicLkOiKSKCLr\nU/xUTqds5bRmyszkNZe7Zh/d4JryomYWztFTRB5y/f6IiFyd4rUPRKR2Nse5RkTqu3HMCyIScqXX\nNvmXJQqTG8Woav0UP3tz6LoPqOp1OJNNvp3Zg1U1QlWnuzYfAa5O8doTqrolW6K8GOck3IvzBcAS\nhckySxTGJ7hqDj+IyDrXz82plLlWRH511UI2ikgN1/4HU+yfIiL+GVxuJVDddWxL1xoGv7vm+g9y\n7R8pF9cAGe3a96qI/J+I3Isz59Z/Xdcs6KoJNHTVOpI/3F01jwlZjPNnUkzoJiKTRSRSnLUnXnPt\n64OTsJaJyDLXvtYi8rPrPn4pIoUzuI7J5yxRmNyoYIpmp9mufYeBVqp6PdAVGJ/KcT2BcapaH+eD\nOso1XUNXoKlrfyLwQAbXvxP4XUSCgWlAV1WtizOTwTMiEgbcDVyrqvWAESkPVtVZQCTON//6qhqT\n4uWvXMde0BWYmcU42+JM03HBYFVtCNQDmotIPVUdjzOl9m2qeptrKo8hwB2uexkJ9MvgOiafy5VT\neJh8L8b1YZlSIDDB1SafiDNv0eV+BgaLSHnga1XdKSItgRuANa7pTQriJJ3U/FdEYoC9ONNQ1wT2\nqOoO1+ufAM8CE3DWuvhQROYB89x9Y6p6RER2u+bZ2QnUAn5ynTczcRbAmbYl5X36l4g8hfPvuizO\nAj0bLzu2sWv/T67rFMC5b8akyRKF8RV9gUPAdTg14X8sSqSqn4rIL0AHYIGIPI2zktcnqjrIjWs8\nkHICQREJTa2Qa26hRjiTzN0L9AZuz8R7mQn8C9gGzFZVFedT2+04gbU4/RPvAfeISBXg/4AbVfWE\niEzDmfjucgIsVtXumYjX5HPW9GR8RTHgoGv9gB44k79dQkSqArtdzS1zcJpglgL3ikhpV5lQcX9N\n8e1AZRGp7truAaxwtekXU9UFOAnsulSOPY0z7XlqZuOsNNYdJ2mQ2ThdE9q9DDQWkVo4q7edBaJF\npAzQLo1YVgNNL7wnESkkIqnVzoxJZonC+IpJwMMisgGnueZsKmX+BWwSkfU461JMd400GgJ8LyIb\ngcU4zTIZUtVYnNk1vxSR34EkIALnQ3ee63w/knob/zQg4kJn9mXnPQFsBSqp6q+ufZmO09X38Q7O\nrLAbcNbH3gZ8itOcdcFUYJGILFPVIzgjsj5zXednnPtpTJps9lhjjDHpshqFMcaYdFmiMMYYky5L\nFMYYY9JlicIYY0y6LFEYY4xJlyUKY4wx6bJEYYwxJl3/D6cFuvMT0Z+1AAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["AUC:0.9980267883550936\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"LJ3LnaTl-Jhp","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":68},"outputId":"b68211e9-874d-4b23-d589-d4f8e40b1478","executionInfo":{"status":"ok","timestamp":1578578864609,"user_tz":0,"elapsed":406959,"user":{"displayName":"Andrei Roibu","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDUCsIj5qwCoMqexSria42B1d9boiojvcfQgzmzXA=s64","userId":"06542274079142548932"}}},"source":["multiple_model_evaluation(neural_network, X, y, title='Neural Network')"],"execution_count":28,"outputs":[{"output_type":"stream","text":["The average test set score for  Neural Network is:  0.9991542527630948\n","The average test log-loss for  Neural Network is:  0.026555752998710852\n","The average AUC for  Neural Network is:  0.998776355262602\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"-6ZDigxdg5lo","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}