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Introducing TreeNet®

This guide describes the TreeNet product and illustrates some practical examples of its basic usage and approach.

Introduction to Tree-Based Machine Learning

The following videos cover the underlying methods in the SPM® 8.2 Software Suite and provide demonstrations of each of the modeling engines.

Software Featured in the Videos:

  • SPM® 8.2 Software Suite
  • CART® Software
  • RandomForests® Software
  • TreeNet® Software
  • MARS® Software
  • RuleLearner™ Software
  • ISLE© Software
  • GeneralizedPathSeeker™ Software

Software Demonstrations

resources software demonstrations

The videos contains the demonstrations of the techniques using the SPM® Software Suite. Software Featured in the Videos: SPM® Software Suite, CART® Software, Random Forests® Software, TreeNet® Software, MARS® Software, RuleLearner® Software, ISLE© Software, Generalized PathSeeker™ Software.

SPM® 8.2 Software Suite Demonstrations

Introduction to SPM® 8.2 Software & Exploring Data

 

A Fast Introduction to RandomForests® Software

 

CART® Software For Regression: Part I

 
This video provides an introduction to CART® software using the SPM® 8.2 Software Suite.

Introduction to MARS® Software for Regression

 

Introduction to TreeNet® Software for Binary Classification

 

Scoring New Data (Generate Predictions)

 
Table of Contents: click the button to the left of the full screen button (hover your mouse over the lower right hand corner of the video)

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TreeNet®

TreeNet

TreeNet® Introduction

Predictive Power

TreeNet® Gradient Boosting is Salford Predictive Modeler’s most flexible and powerful data mining tool, capable of consistently generating extremely accurate models. The TreeNet modeling engine’s level of accuracy is usually not attainable by single models or by ensembles such as bagging or conventional boosting. The TreeNet engine demonstrates remarkable performance for both regression and classification. The algorithm typically generates thousands of small decision trees built in a sequential error–correcting process to converge to an accurate model. The TreeNet modeling engine has been responsible for the majority of Minitab’s modeling competition awards.

Supreme Accuracy

The TreeNet® modeling engine adds the advantage of a degree of accuracy usually not attainable by a single model or by ensembles such as bagging or conventional boosting. As opposed to neural networks, the TreeNet methodology is not sensitive to data errors and needs no time-consuming data preparation, pre-processing or imputation of missing values. This type of data error can be very challenging for conventional data mining methods and will be catastrophic for conventional boosting. In contrast, the TreeNet model is generally immune to such errors as it dynamically rejects training data points too much at variance with the existing model. The TreeNet modeling engine robustness extends to data contaminated with erroneous target labels.

Advanced Features

Interaction detection establishes whether interactions of any kind are needed in a predictive model, and is a search engine discovering specifically which interactions are required. The interaction detection system not only helps improve model performance (sometimes dramatically) but also assists in the discovery of valuable new segments and previously unrecognized patterns.

Technical Articles by Jerome Friedman are also available for download:

 

 

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TreeNet®: Supported File Types

The TreeNet® data-translation engine supports data conversions for more than 80 file formats, including popular statistical-analysis packages such as SAS® and SPSS®, databases such as Oracle and Informix, and spreadsheets such as Microsoft Excel and Lotus 1-2-3.

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