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(actual_te != predicted)/length(predicted) # Overall error rate
##  0.1574153
round(100*table(actual_te, predicted, dnn=c("Actual", "Predicted"))/length(predicted))
## Predicted ## Actual No Yes ## No 74 5 ## Yes 11 10
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