Ensemble Models of Rank-Based Trees with Extracted Decision Rules
Extract Interpretable Decision Rules from a Random Forest Model
Variable Importance Index for Each Predictor
Transform Continuous Variables into Ranked Binary Pairs
Prediction or Extract Predicted Values for Random Forest, Random Fores...
Ensemble Models of Rank-Based Trees for Single Sample Classification w...
Generalized Boosted Modeling via Rank-Based Trees for Single Sample Cl...
Random Forest via Rank-Based Trees for Single Sample Classification wi...
Select Decision Rules to Achieve Higher Prediction Accuracy
Fast computing an ensemble of rank-based trees via boosting or random forest on binary and multi-class problems. It converts continuous gene expression profiles into ranked gene pairs, for which the variable importance indices are computed and adopted for dimension reduction. Decision rules can be extracted from trees.
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