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.
This exercise is part of the course
Predicting CTR with Machine Learning in Python
Exercise instructions
- Create the list of values
[10, 20]formax_iter, and a list of values[(8, ), (16, )]forhidden_layer_sizes. - Set up a grid search with 4 jobs using
n_jobsto iterate over all hyperparameter combinations. - Print out the best AUC score, and the best estimator that led to this score.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# 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.____)