Results analysis

Having successfully trained our MLP, let's evaluate our model based on the testing accuracy, confusion matrix, and receiver operating characteristic (ROC) curve.

Testing accuracy

We can evaluate our model on the training set and testing set using the evaluate() function:

scores = model.evaluate(X_train, y_train)
print("Training Accuracy: %.2f%%\n" % (scores[1]*100))

scores = model.evaluate(X_test, y_test)
print("Testing Accuracy: %.2f%%\n" % (scores[1]*100))

We get the following result:

The accuracy is 91.85% and 78.57% on the training set and testing set respectively. The difference in accuracy between the training and testing set isn't surprising since the model was trained on...