SPM8 Features – Data Tools

SALFORD PREDICTIVE MODELER

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: MARS® nonlinear regression splines
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
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
- 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

CART®

SPM’s CART® modeling engine is the ultimate classification tree that has revolutionized the field of advanced analytics, and inaugurated the current era of data science.

Random Forests®

Random Forests® is a modeling engine that leverages the power of multiple alternative analyses, randomization strategies, and ensemble learning.

MARS®

The MARS® modeling engine is ideal for users who prefer results in a form similar to traditional regression while capturing essential nonlinearities and interactions.

TreeNet®

TreeNet® Gradient Boosting is SPM’s most flexible and powerful data mining tool, capable of consistently generating extremely accurate models.

Pricing

University Program

Our University Program provides the SPM®, CART®, MARS®, TreeNet® , and Random Forests® modeling engines at significantly-reduced licensing fees to the educational community.

Automation

70+ pre-packaged scenarios, basically experiments, inspired by how leading model analysts structure their work.

DataTools