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  3. Predicting CTR with Machine Learning in Python

Exercise

Varying hyperparameters

The number of iterations of training, and the size of hidden layers are two primary hyperparameters that can be varied when working with a MLP classifier. In this exercise, you will vary both separately and note how performance in terms of accuracy and AUC of the ROC curve may vary.

X_train, y_train, X_test, y_test are available in your workspace. Features have already been standardized using a StandardScaler(). pandas as pd, numpy as np are also available in your workspace.

Instructions 1/2

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  • 1
    • Create a MLP classifier for each configuration of max_iter.
    • Calculate accuracy and AUC using roc_auc_score() on y_score.
  • 2
    • Create 3 MLP classifiers with 4, 8, and 16 units (using (4, ), (8, ), etc.)
    • Derive accuracy and AUC of ROC for each.