Features List 

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