Unbiased variable importance for random forests
نویسندگان
چکیده
منابع مشابه
Variable selection using random forests
This paper proposes, focusing on random forests, the increasingly used statistical method for classification and regression problems introduced by Leo Breiman in 2001, to investigate two classical issues of variable selection. The first one is to find important variables for interpretation and the second one is more restrictive and try to design a good prediction model. The main contribution is...
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One of the main topic in the development of predictive models is the identification of variables which are predictors of a given outcome. Automated model selection methods, such as backward or forward stepwise regression, are classical solutions to this problem, but are generally based on strong assumptions about the functional form of the model or the distribution of residuals. In this paper a...
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Figure: Averaged normalized importances for X1 from 100 simulated datasets (simulation process described below) for m=1,2,3,4 (left to right) with β1=(4,1,1,0.3) , corr(Xj,Xk)=ρ |j−k| with ρ=−0.9 to 0.9 in steps of 0.1 Grey line: true normalized LMG allocation; Black line: true normalized PMVD allocation : Variable importance (% MSE Reduction) from RF-CART; ×: Variable importance (% MSE Reducti...
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ژورنال
عنوان ژورنال: Communications in Statistics - Theory and Methods
سال: 2020
ISSN: 0361-0926,1532-415X
DOI: 10.1080/03610926.2020.1764042