CART
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CART Features

Additional CART Features are available in Pro, ProEx, and Ultra.

ComponentsBasicProProExUltra
ComponentsBasicProProExUltra
Modeling Engine:
CART (Decision Trees)
o o o o
Linear Combination Splits o o o o
Optimal tree selection based on area under ROC curve o o o o
User defined splits for the root node and its children   o o o
Automation: Generate models with alternative handling of missing values (Battery MVI)   o o o
Automation: RULES: build a model using each splitting rule (six for classification, two for regression).   o o o
Automation: Build a series of models using all available splitting strategies (six for classification, two for regression) (Battery RULES)   o o o
Automation: Build a series of models varying the depth of the tree (Battery DEPTH)   o o o
Automation: Build a series of models changing the minimum required size on parent nodes (Battery ATOM)   o o o
Automation: Build a series of models changing the minimum required size on child nodes (Battery MINCHILD)   o o o
Automation: Explore accuracy versus speed trade-off due to potential sampling of records at each node in a tree (Battery SUBSAMPLE)   o o o
Multiple user defined lists for linear combinations     o o
Constrained trees     o o
Ability to create and save dummy variables for every node in the tree during scoring     o o
Report basic stats on any variable of user choice at every node in the tree     o o
Comparison of learn vs. test performance at every node of every tree in the sequence     o o
Hot-Spot detection to identify the richest nodes across multiple trees     o o
Automation: Vary the priors for the specified class (Battery PRIORS)     o o
Automation: Build a series of models limiting the number of nodes in a tree (Battery NODES)     o o
Automation: Build a series of models trying each available predictor as the root node splitter (Battery ROOT)     o o
Automation: Explore the impact of favoring equal sized child nodes (Battery POWER)     o o
Automation: Vary the priors for the specified class (Battery PRIORS)     o o
Automation: Build a series of models by progressively removing misclassified records thus increasing the robustness of trees and posssibly reducing model complexity (Battery REFINE)     o o
Automation: Bagging and ARCing using the legacy code (COMBINE)     o o
Build a CART tree utilizing the TreeNet engine to gain speed as well as alternative reporting       o
Build a Random Forests model utlizing the CART engine to gain alternative handling of missing values via surrogate splits (Battery BOOTSTRAP RSPLIT)       o

 additional cart features

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Tags: CART, Classification and Regression Trees, Salford-Systems

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