Evaluating the Model Performance

Let us see how well our model has learned from the train data, by testing it on the test data.


  • model.evaluate(X_test, y_test) : Returns a list with the loss(at index 0) and accuracy(at index 1) of the predictions of the model on X_test.

  • array[a:b] : Returns the elements of array from index a till b-1 inclusive.

  • model.predict : Generates output predictions for the input samples. It returns the probabilities of each class for a given test sample. We consider the classification to be of that class(or of that index) whose probability of the highest.

  • np.argmax : Returns the indices of the maximum values along an axis. More here.

    For example,

    >> a = array([[10, 11, 12],
              [13, 14, 15]])
    >> np.argmax(a)
    >> np.argmax(a, axis=0)
    array([1, 1, 1])
    >> np.argmax(a, axis=1)
    array([2, 2])
  • Use model.evaluate on X_test, y_test to see the test data prediction accuracy:

    results = << your code comes here >>(X_test, y_test)
  • Print the accuracy on the test data.

  • Let us see the predictions of 9 of the test samples from index 10.

    • Slice the X_test to get 9 of the data samples of the test data from index 10.

      X_new = << your code comes here >>
    • Use model.predict on X_new and get the predictions in y_pred.

      y_pred = << your code comes here >>(X_new)
    • Use np.argmax on y_pred along the axis=1 and print the classes predicted by the model on X_new.

      print(<< your code comes here >>(y_pred, axis=1))
    • Let us also look at the original classes of X_new.

      print (y_test[10:20])

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