initializations to get better understanding of neural networks on real-world datalet' apply the mlpclassifier to the breast cancer dataset we start with the default parametersin[ ]print("cancer data per-feature maxima:\ {}format(cancer data max(axis= ))out[ ]cancer data per-feature maxima in[ ]x_trainx_testy_trainy_test train_test_splitcancer datacancer targetrandom_state= mlp mlpclassifier(random_state= mlp fit(x_trainy_trainprint("accuracy on training set{ }format(mlp score(x_trainy_train))print("accuracy on test set{ }format(mlp score(x_testy_test))out[ ]accuracy on training set accuracy on test set the accuracy of the mlp is quite goodbut not as good as the other models as in the earlier svc examplethis is likely due to scaling of the data neural networks also expect all input features to vary in similar wayand ideally to have mean of and supervised learning