Data Science Desktop Survival Guide by Graham Williams
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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, risks)

## Error in na.omit(ac): object 'actual' not found

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 != predicted)/length(predicted) # Overall error rate

## Error in eval(expr, envir, enclos): object 'actual' not found

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

## Error in table(actual, predicted, dnn = c("Actual", "Predicted")): object 'actual' not found