Data considerations for Fit Model and Discover Key Predictors with TreeNet^® Regression

To ensure that your results are valid, consider the following guidelines when you collect data, perform the analysis, and interpret your results.

The response variable should be continuous

A continuous variable can be measured and ordered, and has an infinite number of values between any two values. For example, the diameters of a sample of tires is a continuous variable.

The data for the response variable must be numeric values.

If your response variable is categorical, use Fit Model or Discover Key Predictors for TreeNet^® Classification.

Predictor variables may be continuous or categorical

You can use a combination of continuous or categorical predictors; however, the column lengths for each predictor must be the same length as the response column. Missing values are allowed.

• All continuous predictors must be numeric.
• Categorical predictors can be text or numeric values.

A test set is recommended when the number of cases > 2000

By default, Minitab uses cross-validation when the number of cases is ≤ 2000. When the number of cases is larger than 2000, Minitab uses a test set. Usually, cross-validation is a better validation method, but requires more time to calculate the results. Validation with a test set is useful when the cross-validation method is too time-consuming.

To learn more about the settings for validation techniques in Fit Model and Discover Key Predictors, go to Specify the validation method for Fit Model and Discover Key Predictors with TreeNet® Regression.