Classification metrics

Although we looked at the test set accuracy for our model, we know from Chapter 1, Gearing Up for Predictive Modeling, that the binary confusion matrix can be used to compute a number of other useful performance metrics for our data, such as precision, recall, and the F measure.

We'll compute these for our training set now:

> (confusion_matrix <- table(predicted = train_class_predictions, actual = heart_train$OUTPUT))
         actual
predicted   0   1
        0 118  16
        1  10  86
> (precision <- confusion_matrix[2, 2] / sum(confusion_matrix[2,]))
[1] 0.8958333
> (recall <- confusion_matrix[2, 2] / sum(confusion_matrix[,2]))
[1] 0.8431373
> (f = 2 * precision * recall / (precision + recall))
[1] 0.8686869 

Here, we used the trick of bracketing our assignment ...

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