20.63 Conditional Decision Tree Performance

Here we plot the performance of the decision tree, showing a risk chart. The areas under the recall and risk curves are also reported.

predicted <- predict(model, ds[te, vars], type="prob")[,2]
riskchart(predicted, actual_te, risk_te)

## Warning in ggplot2::guide_legend(keywidth = 3, labels = 1:3, title = "Legend"): Arguments in `...` must be used.
## ✖ Problematic argument:
## • labels = 1:3
## ℹ Did you misspell an argument name?
## Arguments in `...` must be used.
## ✖ Problematic argument:
## • labels = 1:3
## ℹ Did you misspell an argument name?

An error matrix shows, clockwise from the top left, the percentages of true negatives, false positives, true positives, and false negatives.

predicted <- predict(model, ds[te, vars], type="response")
sum(actual_te != predicted)/length(predicted) # Overall error rate

## [1] 0.1546766

round(100*table(actual_te, predicted, dnn=c("Actual", "Predicted"))/length(predicted))

##       Predicted
## Actual No Yes
##    No  74   4
##    Yes 11  10

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