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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.

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Predicting CTR with Machine Learning in Python

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Интерактивное практическое упражнение

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# Loop over various max_iter configurations
max_iter_list = [10, 20, 30]
for max_iter in ____:
	clf = MLPClassifier(hidden_layer_sizes = (4, ), 
                        ____ = max_iter, random_state = 0)
   	# Extract relevant predictions
	y_score = clf.fit(____, ____).____(X_test)
	y_pred = clf.fit(____, ____).____(X_test)

	# Get ROC curve metrics
	print("Accuracy for max_iter = %s: %s" %(
      max_iter, _____(y_test, ____)))
	print("AUC for max_iter = %s: %s" %(
      max_iter, ____(y_test, ____[:, 1])))
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