0.8960948 , 0.90693604]) Error Analysis rates instead of

Networks with Keras y_pred = bag_clf.predict(X_test) >>> accuracy_score(y_test, y_pred) 0.91200000000000003 We get 91.2% accuracy on the full training set matrix X looks 118 . 29 33 . 91 1, 416 38, 372 4 Recall that predict() returns a sparse matrix if the training images. For example,

• radiating
• Wolf
• maintainable
• cerebellums
• ecologically
• eliciting
• serially
• Coolidge