GridSearchCV MPLRegressor
%%time
params = {'activation': ['relu', 'tanh', 'logistic', 'identity'],
'hidden_layer_sizes': list(itertools.permutations([50,100,150],2)) + list(itertools.permutations([50,100,150],3)) + [50,100,150],
'solver': ['adam', 'lbfgs'],
'learning_rate' : ['constant', 'adaptive', 'invscaling']
}
mlp_regressor_grid = GridSearchCV(MLPRegressor(random_state=123), param_grid=params, n_jobs=-1, cv=5, verbose=5)
mlp_regressor_grid.fit(X_train,Y_train)
print('Train R^2 Score : %.3f'%mlp_regressor_grid.best_estimator_.score(X_train, Y_train))
print('Test R^2 Score : %.3f'%mlp_regressor_grid.best_estimator_.score(X_test, Y_test))
print('Best R^2 Score Through Grid Search : %.3f'%mlp_regressor_grid.best_score_)
print('Best Parameters : ',mlp_regressor_grid.best_params_)