Gradient Boosting

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TreeNet Introduction

TreeNet Introduction

Predictive Power:
TreeNet is Salford's most flexible and powerful data mining tool, capable of consistently generating extremely accurate models. TreeNet’s level of accuracy is usually not attainable by single models or by ensembles such as bagging or conventional boosting. TreeNet 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. Tree Net has been responsible for the majority of Salford’s modeling competition awards.
Supreme Accuracy:
TreeNet's robustness extends to data contaminated with erroneous target labels. This type of data error can be very challenging for conventional data mining methods and will be catastrophic for conventional boosting. In contrast, TreeNet is generally immune to such errors as it dynamically rejects training data points too much at variance with the existing model. In addition, TreeNet 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, TreeNet is not sensitive to data errors and needs no time-consuming data preparation, preprocessing or imputation of missing values.
Advanced Features:
Interaction detection establishes whether interactions of any kind are needed in a predictive model, and is a search engine to discover 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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Tags: TreeNet, Salford-Systems

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