Testing the classifier

Let's see for ourselves by calculating the accuracy score on the training set:

In [19]: ret, y_pred = lr.predict(X_train)
In [20]: metrics.accuracy_score(y_train, y_pred)
Out[20]: 1.0

Perfect score! However, this only means that the model was able to perfectly memorize the training dataset. This does not mean that the model would be able to classify a new, unseen data point. For this, we need to check the test dataset:

In [21]: ret, y_pred = lr.predict(X_test)
...      metrics.accuracy_score(y_test, y_pred)
Out[21]: 1.0

Luckily, we get another perfect score! Now we can be sure that the model we built is truly awesome.