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Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests

Journal of Machine Learning Research, (2016)

Cited by: 130|Views52
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Abstract

This work develops formal statistical inference procedures for machine learning ensemble methods. Ensemble methods based on bootstrapping, such as bagging and random forests, have improved the predictive accuracy of individual trees, but fail to provide a framework in which distributional results can be easily determined. Instead of agg...More

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