Use CART® Classification to create a decision tree for a binomial or multinomial categorical response with many categorical and continuous predictor variables.

Minitab’s CART engine for classification offers an intuitive interface that can handle a binary outcome (two groups) or a multinomial outcome (three or more groups).

The multinomial logistic regression is a logistic regression model having a dependent variable with more than two levels (Agresti (1990), Santer and Duffy (1989), Nerlove and Press (1973)).

Use CART® Classification to use complex relationships with multiple predictors to predict multiple choice survey responses. This example applies to the Customer Contact Center Module. For …

Classification loss functions: binary or multinomial Differential lift (also called “uplift” or “incremental response”) modeling Column subsampling to improve model performance and …

For a multinomial response variable, Minitab displays multiple charts that treat each class as the event in turn.

Minitab provides three link functions: logit (the default), normit, and gompit. The link functions allow you to fit a broad class of ordinal response models. The logit is the inverse of the …

Use the goodness-of-fit tests to determine whether the predicted probabilities deviate from the observed probabilities in a way that the multinomial distribution does not predict.

Use to calculate an exact P-value for the chi-square statistic based on the multinomial distribution. The exact test should be invoked only when there are relatively few categories (2 or 3) and/or …

If the expected counts (also called expected frequencies) for the cells are very small, the results of the test may not be valid. If one or more categories have expected counts that are too low, you …