20.67 Weka 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.
<- 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
##  0.1555086
round(100*table(actual_te, predicted, dnn=c("Actual", "Predicted"))/length(predicted))
## Predicted ## Actual No Yes ## No 74 4 ## Yes 11 10
Your donation will support ongoing availability and give you access to the PDF version of this book. Desktop Survival Guides include Data Science, GNU/Linux, and MLHub. Books available on Amazon include Data Mining with Rattle and Essentials of Data Science. Popular open source software includes rattle, wajig, and mlhub. Hosted by Togaware, a pioneer of free and open source software since 1984. Copyright © 1995-2021 Graham.Williams@togaware.com Creative Commons Attribution-ShareAlike 4.0