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MLP Grid Search

Hyperparameter tuning can be done by sklearn through providing various input parameters, each of which can be encoded using various functions from numpy. One method of tuning, which exhaustively looks at all combinations of input hyperparameters specified via param_grid, is grid search. In this exercise, you will use grid search to look over the hyperparameters for a MLP classifier.

X_train, y_train, X_test, y_test are available in your workspace, and the features have already been standardized. pandas as pd, numpy as np, are also available in your workspace.

Este exercício faz parte do curso

Predicting CTR with Machine Learning in Python

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Instruções do exercício

  • Create the list of values [10, 20] for max_iter, and a list of values [(8, ), (16, )] for hidden_layer_sizes.
  • Set up a grid search with 4 jobs using n_jobs to iterate over all hyperparameter combinations.
  • Print out the best AUC score, and the best estimator that led to this score.

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

# Create list of hyperparameters 
max_iter = [____, ____]
hidden_layer_sizes = [____, ____]
param_grid = {'max_iter': max_iter, 'hidden_layer_sizes': hidden_layer_sizes}

# Use Grid search CV to find best parameters using 4 jobs
mlp = ____
clf = ____(estimator = mlp, param_grid = ____, 
           scoring = 'roc_auc', ____ = 4)
clf.fit(X_train, y_train)
print("Best Score: ")
print(clf.____)
print("Best Estimator: ")
print(clf.____)
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