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Evaluating precision and ROI

In this exercise, you build upon the previous exercise and run an MLPClassifier and compare it to three of the other classifiers run earlier. For each classifier, you will compute the precision and implied ROI on ad spend. As before, we have training and testing splits for X and y as X_train, X_test for X and y_train, y_test for y respectively and the features have already been standardized.

This exercise is part of the course

Predicting CTR with Machine Learning in Python

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Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create list of classifiers
names = ['Logistic Regression',  'Decision Tree',
         'Random Forest', 'Multi-Layer Perceptron']
clfs = [LogisticRegression(), 
        DecisionTreeClassifier(), RandomForestClassifier(), 
        MLPClassifier(hidden_layer_sizes = (5, ), max_iter = 40)]

# Fit each classifier and evaluate AUC of ROC curve 
for name, classifier in zip(names, clfs):
  classifier.____(____, ____)
  y_score = classifier.____(X_test)
  y_pred = classifier.____(X_test) 
  prec = ____(____, y_pred, average = 'weighted')
  print("Precision for %s: %s " %(name, prec))
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