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CART (Classification and Regression Trees) is a type of decision tree analysis that uses a tree-like model to classify data or predict continuous outcomes. The technique was first introduced by Leo Breiman and colleagues in the 1980s. CART is a non-parametric method, meaning it doesn't require any specific distribution of the data. The algorithm works by recursively partitioning the data into smaller subsets based on the values of the predictor variables.

Let me know which interpretation fits, and I can give you step-by-step Minitab instructions or interpret an example output. minitab cart

| Step | Action in Minitab | |------|-------------------| | 1 | Run or Classification | | 2 | In Validation , choose k-fold cross-validation (e.g., 10 folds) | | 3 | After fit, view Variable Importance chart | | 4 | Look for predictors with high importance AND small error bars | | 5 | Check Split History – solid features appear early and repeatedly | CART (Classification and Regression Trees) is a type

This process repeats for each resulting segment until it reaches a "leaf" or terminal node that represents a final prediction or grouping. The algorithm works by recursively partitioning the data

Here are some best practices for using Minitab CART:

Minitab includes (Classification and Regression Trees) as part of its Predictive Analytics Module or Minitab Statistical Software (version 21+ with add-on).