 # Evaluating the Model Performance

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

Note:

• `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)
5

>> np.argmax(a, axis=0)
array([1, 1, 1])

>> np.argmax(a, axis=1)
array([2, 2])
``````
INSTRUCTIONS
• 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.

``````print(results)
``````
• 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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