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.
The researcher can use the gradient boosted classification tree model to predict response class probabilities for new observations.
This example uses the dataset from Fit Model, but prediction is also available when you use Discover Key Predictors to create the model.
| Age | 35 | 35 | |
| Rest Blood Pressure | 140 | 140 | |
| Cholesterol | 233 | 233 | |
| Max Heart Rate | 150 | 165 | |
| Old Peak | 2.3 | 2.3 | |
| Sex | Male | Female | |
| Chest Pain Type | 2 | 1 | |
| Fasting Blood Sugar | True | True | |
| Rest ECG | 0 | 1 | |
| Exercise Angina | |||
| Slope | 1 | 3 | |
| Major Vessels | 0 | 2 | |
| Thal | Normal | Normal |
Minitab uses the gradient boosted classification trees in the results to estimate the class probability of a heart disease event for the a set of prediction values. The researchers find that the probability of a heart disease event using the specified settings is approximately 0.15 for the first set and 0.43 for the second set.