Here we plot the performance of the decision tree, showing a risk chart. The areas under the recall and risk curves are also reported.
<- predict(model, ds[te, vars], type="prob")[,2] predicted riskchart(predicted, actual_te, risk_te)
An error matrix shows, clockwise from the top left, the percentages of true negatives, false positives, true positives, and false negatives.
<- predict(model, ds[te, vars], type="class") predicted sum(ds[te, target] != predicted)/length(predicted) # Overall error rate round(100*table(ds[te, target], predicted, dnn=c("Actual", "Predicted"))/length(predicted))
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