This paper experimentally compares pairwise versions of our measure in binary RF classifiers against Breiman's Gini-based measure using three datasets, a toy ...
Novel variable interaction measures with random forest classifiers are proposed. The proposed methods efficiently measure the change in classification ...
Interaction forests are a variant of random forests for categorical, continuous, and survival outcomes that explicitly models quantitative and qualitative ...
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Variable importance measures for random forests have been receiving increased attention as a means of variable selection in many classification tasks in ...
Mar 31, 2016 · Random forests are generally capable of capturing gene-gene interactions, but current variable importance measures are unable to detect them as interactions.
Embedded methods weave variable importance directly into the machine learning algorithm. For instance, machine learning methods like random forests (RF)[2] and ...
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Apr 13, 2015 · In order to increase the accuracy of my model I decided to include pairwise interaction terms. However, doing so led to a decrease in accuracy.
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The operating definition of interaction used is that variables m and k interact if a split on one variable, say m, in a tree makes a split on k either ...
Jan 4, 2022 · Random forests, for example, involves the use of the Gini coefficient, and the reduc- tion in mean square error, to catalog a variable's ...
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In addition to high predictive performance, random forest classifiers can reveal feature importance, telling us how much each feature contributes to class ...
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