early_stopping=True))
est.fit(X_train, y_train)
print("done in {:.3f}s".format(time() - tic))
print("Test R2 score: {:.2f}".format(est.score(X_test, y_test)))
// %%
// We configured a pipeline to scale the numerical input features and tuned the
// neural network size and learning rate to get a reasonable compromise between
After Change
"Partial dependence of house value on non-location features\n"
"for the California housing dataset, with Gradient Boosting"
)
display.figure_.subplots_adjust(wspace=0.4, hspace=0.3)
// %%
// Analysis of the plots
// .....................
