This command is available with the Predictive Analytics Module. Click here for more information about how to activate the module.
A team of researchers collects and publishes detailed information about factors that affect heart disease. Variables include age, sex, cholesterol levels, maximum heart rate, and more. This example is based on a public data set that gives detailed information about heart disease. The original data are from archive.ics.uci.edu.
After initial exploration with CART® Classification to identify the important predictors, the researchers use both TreeNet® Classification and Random Forests® Classification to create more intensive models from the same data set. The researchers compare the model summary table and the ROC plot from the results to evaluate which model provides a better prediction outcome. For results from the other analyses, go to Example of tree creation with CART® Classification and Example of Fit Model with TreeNet® Classification.
For this analysis, the number of observations is 303. Each of the 300 bootstrap samples uses the 303 observations to create a tree. The data includes a good split of nonevents and events.