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TreeNet® Tree Ensembles and CART® Decision Trees: A Winning Combination

TreeNet Tree Ensembles and CART Decision Trees: A Winning Combination

TreeNet Tree Ensembles and CART Decision Trees: A Winning Combination

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Combining CART decision trees with TreeNet stochastic gradient boosting: A winning combination.

Learn about how you can combine the best of both tools in this 1 hour webinar.

 

Course Outline

 

I. Classification and Regression Trees Pros/Cons

II. Stochastic Gradient Boosting: a promising way to overcome the shortcomings of a single tree

III. Introducing Stochastic Gradient Boosting, a powerful modern ensemble of boosted trees

  • Methodology
  • Reporting
  • Interpretability
  • Post-Processing
  • Interaction Detection

IV. Advantages of using both Classification and Regression Trees and Tree Ensembles

 

Watch the Video

TreeNet Tree Ensembles and CART Decision Trees

 

TreeNet Tree Ensembles and CART Decision Trees: A Winning Combination

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The Evolution of Regression Modeling: from Classical Linear Regression to Modern Ensembles

The Evolution of Regression Modeling: from Classical Linear Regression to Modern Ensembles

Webinar Title: The Evolution of Regression Modeling: from Classical Linear Regression to Modern Ensembles

Date/Time: Friday, March 1, 15, 29, and April 12 2013, 10am-11am, PST


Course Description:
Regression is one of the most popular modeling methods, but the classical approach has significant problems. This webinar series address these problems. Are you are working with larger datasets? Is your data challenging? Does your data include missing values, nonlinear relationships, local patterns and interactions? This webinar series is for you! We will cover improvements to conventional and logistic regression, and will include a discussion of classical, regularized, and nonlinear regression, as well as modern ensemble and data mining approaches. This series will be of value to any classically trained statistician or modeler.

Part 1

Part 1: Regression methods discussed (download slides)

  • Classical Regression
  • Logistic Regression
  • Regularized Regression: GPS Generalized Path Seeker
  • Nonlinear Regression: MARS Regression Splines

The Evolution of Regression Modeling

 

The Evolution of Regression Modeling: from Classical Linear Regression to Modern Ensembles

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Advances in Gradient Boosting: The Power of Post Processing

Advances in Gradient Boosting: The Power of Post Processing

Click to View / Download PDF

Advances in Gradient Boosting: the Power of Post-Processing

Learn how TreeNet stochastic gradient boosting can be improved by post processing techniques such as GPS Generalized Path Seeker, RuleLearner, and ISLE.

Course Outline:

I. Gradient Boosting and Post-Processing:

  • What is missing from Gradient Boosting?
  • Why post-processing techniques are used?

II. Applications Benefiting from Post-Processing: Examples from a variety of industries.

  • Financial Services
  • Biomedical
  • Environmental
  • Manufacturing
  • Adserving

III. Typical Post-Processing Steps

 

IV. Techniques

  • Generalized Path Seeker (GPS): Modern high-speed LASSO-style regularized regression
  • Importance Sampled Learning Ensembles (ISLE): identify and reweight the most influential trees
  • RuleLearner: ISLE on “steroids.” Identify the most influential nodes and rules

V. Case Study Example

  • Output/Results without Post-Processing
  • Output/Results with Post-Processing
  • Demo

Watch the Video

 

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Algorithms

Algorithms

Components and Features

Download Components and Features 

SPM Components and FeaturesWhat's New
SPM Components and FeaturesWhat's New
CART (Classification and Regression Trees) User defined linear combination lists for splitting; Constrains on trees; Automatic addition of missing value indicators; Enhanced GUI reporting; User controlled Cross Validation; Out-of-bag performance stats and predictions; Profiling terminals nodes based on user supplied variables; Comparison of Train vs. Test consistency across nodes; RandomForests-style variable importance
MARS (Automated Nonlinear Regression) Updated GUI interface; Model performance based on independent test sample or Cross Validation; Support for time series models
TreeNet (Gradient Boosting, Boosted Trees) One-Tree TreeNet (CART alternative); RandomForests via TreeNet (RandomForests regression alternative) Interaction Control Language (ICL); Interaction strength reporting; Enhanced partial dependency plots; RandomForests-style randomized splits;
RandomForests (Bagging Trees) RandomForests regression; Saving out-of-bag scores; Speed enhancements
High-Dimensional Multivariate Pattern Discovery Battery Target (link) to identify mutual dependencies in the data
Unsupervised Learning (Breiman's Column Scrambler) New
Text Mining New
Model Compression and Rule Extraction New: ISLE; RuleLearner; Hybrid Compression
Automation 56 pre-packaged scenarios based on years of high-end consulting
Parallel Processing New: Automatic support of multiple cores via multithreading
Interaction Detection  
Hotspot Detection Segment Extraction (Battery Priors)
Missing Value Handling and Imputation  
Outlier Detection New: GUI reports, tables, and graphs
Linear Methods for Regression, Recent Advances and Discoveries New: OLS Regression; Regularized Regression Including: LAR/LASSO Regression; Ridge Regression; Elastic Net Regression/ Generalized Path Seeker
Linear Methods for Classification, Recent Advances and Discoveries New: LOGIT; LAR/LASSO; Ridge; Elastic Net/ Generalized Path Seeker
Model Assessment and Selection Unified reporting of various performance measures across different models
Ensemble Learning New: Battery Bootstrap; Battery Model
Time Series Modeling New
Model Simplification Methods   
Data Preparation New: Battery Bin for automatic binning of a user selected set of variables with large number of options
Large Data Handling 64 bit support; Large memory capacity limited only by your hardware
Model Translation (SAS, C, Java, PMML, Classic) Java
Data Access (all popular statistical formats supported) Updated Stat Transfer Drivers including R workspaces
Model Scoring Score Ensemble (combines multiple models into a powerful predictive machine)

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