DataTools
Menu
Menu
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
Contact us for pricing information.
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.