Pages that link to "Item:Q1630843"
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The following pages link to A computationally fast variable importance test for random forests for high-dimensional data (Q1630843):
Displaying 15 items.
- vita (Q38727) (← links)
- Correlation and variable importance in random forests (Q58729) (← links)
- Random subspace method for high-dimensional regression with the \texttt{R} package \texttt{regRSM} (Q311298) (← links)
- A new variable importance measure for random forests with missing data (Q892440) (← links)
- Random forest for ordinal responses: prediction and variable selection (Q1659465) (← links)
- Grouped variable importance with random forests and application to multiple functional data analysis (Q1663198) (← links)
- On the use of random forest for two-sample testing (Q2129579) (← links)
- Interaction forests: identifying and exploiting interpretable quantitative and qualitative interaction effects (Q2129608) (← links)
- Techniques to improve ecological interpretability of black-box machine learning models. Case study on biological health of streams in the United States with gradient boosted trees (Q2163504) (← links)
- A new variable selection approach using random forests (Q2361222) (← links)
- Random forests in count data modelling: an analysis of the influence of data features and overdispersion on regression performance (Q2684565) (← links)
- Variable selection and importance in presence of high collinearity: an application to the prediction of lean body mass from multi-frequency bioelectrical impedance (Q5861598) (← links)
- Consistent and unbiased variable selection under indepedent features using random forest permutation importance (Q6103229) (← links)
- Efficient permutation testing of variable importance measures by the example of random forests (Q6113742) (← links)
- Exploratory identification of predictive biomarkers in randomized trials with normal endpoints (Q6627504) (← links)
