SPM Features
Features
Salford Predictive Modeler® 8 General Features:
- Modeling Engine: CART® decision trees
- Modeling Engine: TreeNet® gradient boosting
- Modeling Engine: Random Forests® tree ensemble
- Modeling Engine: GPS regularized regression (LASSO, Elastic Net, Ridge, etc.)
- Modeling Engine: RuleLearner®, incorporating TreeNet's accuracy plus the interpretability of regression
- Modeling Engine: ISLE model compression
- 70+ pre-packaged automation routines for enhanced model building and experimentation
- International number and date displays.
- Tools to relieve gruntwork, allowing the analyst to focus on the creative aspects of model development.
- Open Minitab Worksheet (.MTW) functionality
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:
o Automatic recursive feature elimination for advanced variable selection
o Experiment with the prior probabilities to obtain a model that achieves better accuracy rates for the more important class
o Perform repeated cross validation
o Build CART models on bootstrap samples
o 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.
o 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
- Automatic recursive feature elimination for advanced variable selection
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