Interpreting tree ensembles with inTrees (2014-08-22T00:00:00.000000Z)

TL;DR

This work provides the interpretable trees (inTrees) framework that extracts, measures, prunes, selects, and summarizes rules from a tree ensemble, and calculates frequent variable interactions.

Abstract

Tree ensembles such as random forests and boosted trees are accurate but difficult to understand. In this work, we provide the interpretable trees (inTrees) framework that extracts, measures, prunes, selects, and summarizes rules from a tree ensemble, and calculates frequent variable interactions. The inTrees framework can be applied to multiple types of tree ensembles, e.g., random forests, regularized random forests, and boosted trees. We implemented the inTrees algorithms in the “inTrees” R package.

Authors

Houtao Deng

1 papers

TL;DR

Abstract

Authors

References41 items

Predicción y selección de variables con bosques aleatorios en presencia de variables correlacionadas

Interpreting tree ensembles with inTrees

A Survey of Methods for Explaining Black Box Models

Discovery of cell-type specific DNA motif grammar in cis-regulatory elements using random Forest

ForEx++: A New Framework for Knowledge Discovery from Decision Forests

Using quantitative features extracted from T2-weighted MRI to improve breast MRI computer-aided diagnosis (CAD)

pre: An R Package for Fitting Prediction Rule Ensembles

A comprehensive data mining approach to estimate the rate of penetration: Application of neural network, rule based models and feature ranking

Interpretability via Model Extraction

A Genetic Algorithm for Interpretable Model Extraction from Decision Tree Ensembles

Interpreting Blackbox Models via Model Extraction

Interpreting Models via Single Tree Approximation

Empirical search for factors affecting mean particle size of PLGA microspheres containing macromolecular drugs

Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach

Making Tree Ensembles Interpretable

SSD Failures in Datacenters: What? When? and Why?

Fatty Acid Methyl Ester (FAME) composition used for estimation of biodiesel cetane number employing random forest and artificial neural networks: A new approach

From Black-Box to Transparent Computational Intelligence Models: A Pharmaceutical Case Study

Modeling the interaction between sensory and affective meanings for detecting metaphor

CBC: An associative classifier with a small number of rules

Guided Random Forest in the RRF Package

Gene selection with guided regularized random forest

Leakage in data mining: formulation, detection, and avoidance

Node harvest: simple and interpretable regression and classication

PREDICTIVE LEARNING VIA RULE ENSEMBLES

Greedy function approximation: A gradient boosting machine.

Integrating Classification and Association Rule Mining

Fast Algorithms for Mining Association Rules in Large Databases

rpart: Recursive partitioning

Classification and Regression by randomForest

CTC : an alternative to extract explanation from bagging

Introduction to arules — Mining Association Rules and Frequent Item Sets

Generalized Boosted Models: A guide to the gbm package

Generalized boosted regression models

Random Forests

Classification and Regression Trees

UCI Repository of machine learning databases

Knowledge Acquisition from Examples Via Multiple Models

Classification and regression

BORN AGAIN TREES

Classification and regression trees

Field of Study

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