Pages that link to "Item:Q2810797"

The following pages link to Quantifying uncertainty in random forests via confidence intervals and hypothesis tests (Q2810797):

Displaying 50 items.

• All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously (Q97217) (← links)
• Testing conditional independence in supervised learning algorithms (Q113672) (← links)
• Estimating the algorithmic variance of randomized ensembles via the bootstrap (Q666594) (← links)
• Generalized random forests (Q666599) (← links)
• Standard errors for bagged and random forest estimators (Q961193) (← links)
• Bootstrap bias corrections for ensemble methods (Q1702284) (← links)
• Statistical uncertainty estimation using random forests and its application to drought forecast (Q1955344) (← links)
• Unrestricted permutation forces extrapolation: variable importance requires at least one more model, or there is no free variable importance (Q2066736) (← links)
• Uncertainty quantification for honest regression trees (Q2072416) (← links)
• Uncertainty quantification for Bayesian CART (Q2073718) (← links)
• Random forest estimation of conditional distribution functions and conditional quantiles (Q2106811) (← links)
• On the use of random forest for two-sample testing (Q2129579) (← links)
• Nonparametric feature selection by random forests and deep neural networks (Q2129580) (← links)
• Rates of convergence for random forests via generalized U-statistics (Q2136608) (← links)
• Data-driven modelling of the Reynolds stress tensor using random forests with invariance (Q2180004) (← links)
• Minimax optimal rates for Mondrian trees and forests (Q2215734) (← links)
• SIRUS: stable and interpretable RUle set for classification (Q2219234) (← links)
• Data-driven polynomial chaos expansion for machine learning regression (Q2220634) (← links)
• Oblique random survival forests (Q2281237) (← links)
• Approximating high-dimensional infinite-order \(U\)-statistics: statistical and computational guarantees (Q2283566) (← links)
• Randomized incomplete \(U\)-statistics in high dimensions (Q2284368) (← links)
• Bootstrapping and sample splitting for high-dimensional, assumption-lean inference (Q2284380) (← links)
• Robust additive Gaussian process models using reference priors and cut-off-designs (Q2307104) (← links)
• Consistent estimation of residual variance with random forest out-of-bag errors (Q2322623) (← links)
• Regression conformal prediction with random forests (Q2512902) (← links)
• A random forest guided tour (Q2629364) (← links)
• Smoothing and adaptation of shifted Pólya tree ensembles (Q2676928) (← links)
• Renewal type bootstrap for increasing degree \(U\)-process of a Markov chain (Q2692922) (← links)
• (Q2934051) (← links)
• Estimation and Inference of Heterogeneous Treatment Effects using Random Forests (Q4559704) (← links)
• Impact of subsampling and tree depth on random forests (Q4615432) (← links)
• (Q4998872) (← links)
• Improving random forest algorithm by Lasso method (Q5033957) (← links)
• Measuring the Algorithmic Convergence of Randomized Ensembles: The Regression Setting (Q5037548) (← links)
• CLT For U-statistics With Growing Dimension (Q5037829) (← links)
• (Q5053199) (← links)
• (Q5053328) (← links)
• Quantifying uncertainty of subsampling-based ensemble methods under a U-statistic framework (Q5055266) (← links)
• Dimension Reduction Forests: Local Variable Importance Using Structured Random Forests (Q5057244) (← links)
• Boosting Random Forests to Reduce Bias; One-Step Boosted Forest and Its Variance Estimate (Q5066399) (← links)
• Local Linear Forests (Q5066400) (← links)
• Random forests for time-dependent processes (Q5140344) (← links)
• (Q5149226) (← links)
• An efficient variance estimator for cross-validation under partition sampling (Q5163049) (← links)
• (Q5214250) (← links)
• Censoring Unbiased Regression Trees and Ensembles (Q5229919) (← links)
• Random Forest Prediction Intervals (Q5869307) (← links)
• Comments on: ``A random forest guided tour'' (Q5972097) (← links)
• Global sensitivity analysis of randomized trials with nonmonotone missing binary outcomes: Application to studies of substance use disorders (Q6079486) (← links)
• Consistent and unbiased variable selection under indepedent features using random forest permutation importance (Q6103229) (← links)