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 statements to simultaneously assign the result of an expression to a variable and print out the value assigned. Now, recall is the ratio of correctly identified instances of class 1, divided...
