Salford Predictive Modeler
Features List
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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
- 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
- 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
- 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
Qsutra® is the Sole Authorised Minitab Partner for Minitab LLC., Minitab Distributor and Minitab Reseller in India, Sri Lanka, Bangladesh & Nepal. Qsutra® provides Minitab’s Training & Technical Support Services to its customers.
We provide services in Data Science areas like Machine Learning, Predictive Analytics, Data Mining and so forth. We also conduct various training programs – Statistical training and Minitab software training. Statistical training starts with basic level to advanced level. Some of the Statistical training certified courses are Predictive Analytics Masterclass, Essential Statistics For Business Analytics, SPC Masterclass, DOE Masterclass, etc. Now coming to Minitab software training, starts with basic to advanced level. Some of the Minitab software training certified courses are Minitab Essentials, Statistical Tools for Pharmaceuticals, Statistical Quality Analysis & Factorial Designs, etc.
Our Minitab Customer Support team is the best place to reach out for any query you may have – Where to buy SPM® 8 Software in India, what is SPM® 8 Pricing model in India, clarity on SPM® 8 Licensing, interested in SPM® 8 Reselling etc.
Write to us at [email protected].