gbt-HIPS: Explaining the Classifications of Gradient Boosted Tree Ensembles

Explaining Naïve Bayes Classifications

Naïve Bayes classifiers, a popular tool for predicting the labels of query instances, are typically learned from a training set. However, since many training sets contain noisy data, a classifier user may be reluctant to blindly trust a predicted label. We present a novel graphical explanation facility for Naïve Bayes classifiers that serves three purposes. First, it transparently explains the ...

Explaining Data-Driven Document Classifications

Many document classification applications require human understanding of the reasons for data-driven classification decisions: by managers, client-facing employees, and the technical team. Predictive models treat documents as data to be classified, and document data are characterized by very high dimensionality, often with tens of thousands to millions of variables (words). Unfortunately, due t...

Optimization of Tree Ensembles

Tree ensemble models such as random forests and boosted trees are among the most widely used and practically successful predictive models in applied machine learning and business analytics. Although such models have been used to make predictions based on exogenous, uncontrollable independent variables, they are increasingly being used to make predictions where the independent variables are cont...

Insurance Premium Prediction via Gradient Tree- Boosted Tweedie Compound Poisson Models

Tree-Structured Boosting: Connections Between Gradient Boosted Stumps and Full Decision Trees

José Marcio Luna 1 Eric Eaton 2 Lyle H. Ungar 2 Eric Diffenderfer 1 Shane T. Jensen 3 Efstathios D. Gennatas 4 Mateo Wirth 3 Charles B. Simone II 5 Timothy D. Solberg 4 Gilmer Valdes 4 1 Dept. of Radiation Oncology, University of Pennsylvania {Jose.Luna,Eric.Diffenderfer}@uphs.upenn.edu 2 Dept. of Computer and Information Science, University of Pennsylvania {eeaton,ungar}@cis.upenn.edu 3 Dept. ...