Features List

CART® Features:

• Hotspot detection to discover the most important parts of the tree and the corresponding tree rules
• Variable importance measures to understand the most important variables in the tree
• Deploy the model and generate predictions in real-time or otherwise
• User defined splits at any point in the tree
• Differential lift (also called “uplift” or “incremental response”) modeling for assessing the efficacy of a treatment
• Automation tools for model tuning and other experiments including
• Automatic recursive feature elimination for advanced variable selection
• Experiment with the prior probabilities to obtain a model that achieves better accuracy rates for the more important class
• Perform repeated cross validation
• Build CART models on bootstrap samples
• Build two linked models, where the first one predicts a binary event while the second one predicts a numeric value. For example, predicting whether someone will buy and how much they will spend.
• Discover the impact of different learning and testing partitions

MARS® Features:

• Graphically understand how variables affect the model response
• Determine the importance of a variable or set of interacting variables
• Deploy the model and generate predictions in real-time or otherwise
• Automation tools for model tuning and other experiments including
• Automatic recursive feature elimination for advanced variable selection
• Automatically assess the impact of allowing interactions in the model
• Easily find the best minimum span value
• Perform repeated cross validation
• Discover the impact of different learning and testing partitions

TreeNet® Features:

• Graphically understand how variables affect the model response with partial dependency plots
• Regression loss functions: least squares, least absolute deviation, quantile, Huber-M, Cox survival, Gamma, Negative Binomial, Poisson, and Tweedie
• Classification loss functions: binary or multinomial
• Differential lift (also called “uplift” or “incremental response”) modeling
• Column subsampling to improve model performance and speed up the runtime.
• Regularized Gradient Boosting (RGBOOST) to increase accuracy.
• RuleLearner: build interpretable regression models by combining TreeNet gradient boosting and regularized regression (LASSO, Elastic Net, Ridge etc.)
• ISLE: Build smaller, more efficient gradient boosting models using regularized regression (LASSO, Elastic Net, Ridge, etc.)
• Variable Interaction Discovery Control
• Determine definitively whether or not interactions of any degree need to be included
• Control the interactions allowed or disallowed in the model with Minitab’s patented interaction control language
• Discover the most important interactions in the model
• Calibration tools for rare-event modeling
• Automation tools for model tuning and other experiments including
• Automatic recursive feature elimination for advanced variable selection
• Experiment with different learn rates automatically
• Control the extent of interactions occurring in the model
• Build two linked models, where the first one predictions a binary event while the second one predicts a numeric value. For example, predicting whether someone will buy and how much they will spend.
• Find the best parameters in your regularized gradient boosting model
• Perform a stochastic search for the core gradient boosting parameters
• Discover the impact of different learning and testing partitions

Random Forests® Features:

• Use for classification, regression, or clustering
• Outlier detection
• Proximity heat map and multi-dimensional scaling for graphically determining clusters in classification problems (binary or multinomial)
• Parallel Coordinates Plot for a better understanding of what levels of predictor values lead to a particular class assignment
• Advanced missing value imputation
• Unsupervised learning: Random Forest creates the proximity matrix and hierarchical clustering techniques are then applied
• Variable importance measures to understand the most important variables in the model
• Deploy the model and generate predictions in real-time or otherwise
• Automation tools for model tuning and other experiments including
• Automatic recursive feature elimination for advanced variable selection
• Easily fine tune the random subset size taken at each split in each tree
• Assess the impact of different bootstrap sample sizes
• Discover the impact of different learning and testing partitions