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Model comparison warmup

In this exercise, you will run a basic comparison of the four categories of outcomes between MLPs and Random Forests using a confusion matrix. This is in preparation for an analysis of all the models we have covered. Doing this warm-up exercise will allow you to compare and contrast the implementation of these models and their evaluation for CTR prediction.

In the workspace, 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. Remember, X contains our engineered features with user, device, and site details whereas y contains the target (whether the ad was clicked). X has already been scaled using a StandardScaler(). For future ad CTR prediction models, the setup will be analogous.

Diese Übung ist Teil des Kurses

Predicting CTR with Machine Learning in Python

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Interaktive Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# Create the list of models in the order below
names = ['Random Forest', 'Multi-Layer Perceptron']
classifiers = [RandomForestClassifier(), 
               ____(____ = (10, ),
                             ____ = 40)]

# Produce a confusion matrix for all classifiers
for name, classifier in zip(names, classifiers):
  print("Evaluating classifier: %s" %(name))
  classifier.fit(____, ____)
  y_pred = classifier.predict(____)
  conf_matrix = confusion_matrix(____, ____)
  print(conf_matrix)
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