• Salford Predictive Modeler

    Features List

    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

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  • Salford Predictive Modeler® 8
    Minitab’s Integrated Suite of Machine Learning Software
    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.

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    Random Forests®

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

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    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.

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    TreeNet®

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

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  • Pricing

    Contact us for pricing information.

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    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.

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    Automation

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

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