Develops a method to estimate quantiles in censored data using random forests.
problem Inability of random forests to handle randomly censored observations.
method Regression adjustment for quantile regression models based on a new estimating equation.
result Consistent estimation of quantiles without parametric modeling assumptions.
New random forest method provides optimal rates and confidence bands.
problem Improving random forest regression rates and constructing confidence bands.
method Proposed Ehrenfest centered purely random forests achieve optimal rates; used Gaussian approximation for supremum of empirical processes.
result Explicit asymptotic uniform confidence bands constructed for both random forest types.
Mondrian random forests improve statistical inference for regression.
problem Improving statistical inference for regression functions.
method Combining bias and variance characterizations with debiasing and variance estimation.
result Valid statistical inference methods for unknown regression functions with error bounds.
Quantile regression using random forest proximities improves prediction and uncertainty quantification.
problem Forecasting corporate bond volume with uncertainty quantification.
method Introduced a novel approach to compute quantile regressions from random forests using proximity metrics.
result Superior performance in approximating conditional target distributions and prediction intervals.
Random forests with attention and self-attention improve regression performance.
problem Improving regression model performance on various datasets.
method Proposes new models using attention and self-attention mechanisms to solve regression problems.
result The models improve model performance on many datasets.
New random forest algorithm improves regression with missing data.
problem Regression with missing data values.
method New random forest algorithm compared to existing techniques.
result Improved performance in quadratic errors and bias compared to existing methods.
The paper uses regression trees/random forests to price Bermudan options more efficiently.
problem Pricing Bermudan options with conditional expectation estimation.
method Estimates conditional expectations using regression trees or random forests instead of traditional regression methods.
result Regression trees/random forests provide better results in high dimensions.
Improved random forest proximities capture data geometry.
problem Inaccurate random forest proximities do not reflect learned data geometry.
method Introduce RF-GAP: Geometry- and Accuracy-Preserving proximities.
result RF-GAP improves geometric representation in tasks like data imputation.
Proposes a new random forest weighted local Fréchet regression method.
problem Complex metric space valued responses and curse of dimensionality in Fréchet regression.
method Locally adaptive kernel generated by random forests for local average and local linear Fréchet regression.
result Significantly improves existing Fréchet regression methods with theoretical guarantees.
We improve random forest consistency and performance with DMRF, a new variant.
problem Improving the consistency and performance of random forest models.
method Developed DMRF, a data-driven multinomial random forest, by modifying proof methods and improving data utilization.
result DMRF achieves strong consistency with probability 1, surpassing previous models in classification tasks.
By seeking the narrowest prediction intervals (PIs) that satisfy the specified coverage probability requirements, the recently proposed quality-based PI learning principle can extract high-quality PIs that better summarize the predictive certainty in regression tasks, and has been widely applied to solve many practical…
We analyze the consistency of decision trees and random forests in regression.
problem Consistency of decision trees and random forests in regression.
method Elementary proofs following classical arguments for smoothing methods.
result Establish weak and almost sure convergence of honest trees and forest averages to the true regression function.
Enhances random forest performance with exogenous randomness.
problem Improving random forest performance through exogenous randomness.
method Developed non-asymptotic MSE expansions for individual trees and forests, identified two types of randomness, and conducted simulations.
result Exogenous randomness, particularly feature subsampling, reduces both bias and variance of random forests.
Random Forests [Breiman:2001] (RF) are a fully non-parametric statistical method requiring no distributional assumptions on covariate relation to the response. RF are a robust, nonlinear technique that optimizes predictive accuracy by fitting an ensemble of trees to stabilize model estimates. The randomForestSRC packag…
CovRegRF estimates covariance matrix from covariates using random forests.
problem Estimating conditional covariances or correlations among multivariate responses.
method Random forest trees with a custom splitting rule to maximize covariance difference.
result Accurate covariance matrix estimates and controlled Type-1 error.
Random forests are powerful non-parametric regression method but are severely limited in their usage in the presence of randomly censored observations, and naively applied can exhibit poor predictive performance due to the incurred biases. Based on a local adaptive representation of random forests, we develop its regre…
Paper introduces a medoid-based approach for efficient Fréchet regression.
problem Regression in metric spaces with random objects.
method Adapted random forest algorithm with medoid-based splitting rule.
result Asymptotic equivalence and consistency of the regression estimator.
Paper proposes a new method combining random forests and Lasso selection.
problem Improving random forest performance by applying Lasso regression.
method Adaptive Lasso weighting applied to random forest predictions.
result Unified framework strictly outperforms other methods in simulations and real-world datasets.
Random forest (RF) methodology is one of the most popular machine learning techniques for prediction problems. In this article, we discuss some cases where random forests may suffer and propose a novel generalized RF method, namely regression-enhanced random forests (RERFs), that can improve on RFs by borrowing the str…
Random forests remain among the most popular off-the-shelf supervised machine learning tools with a well-established track record of predictive accuracy in both regression and classification settings. Despite their empirical success as well as a bevy of recent work investigating their statistical properties, a full and…
Random forest models systematically bias predictions; a numerical transform corrects this.
problem Systematic bias in random forest regression predictions.
method Defined a numerical transformation using training data to correct bias.
result Improved predictions across real-world and synthetic datasets.
Random forests are a powerful method for non-parametric regression, but are limited in their ability to fit smooth signals, and can show poor predictive performance in the presence of strong, smooth effects. Taking the perspective of random forests as an adaptive kernel method, we pair the forest kernel with a local li…
Ensembles of randomized decision trees, usually referred to as random forests, are widely used for classification and regression tasks in machine learning and statistics. Random forests achieve competitive predictive performance and are computationally efficient to train and test, making them excellent candidates for r…
Theoretical study of random forests for nonlinear time series.
problem Theoretical justification for using random forests in time series modeling.
method Uniform concentration inequality for regression trees and random forests consistency proof.
result Consistency of random forests for nonlinear autoregressive processes.
Enhances random forest consistency and introduces DMRF for improved performance.
problem Improving the consistency and efficiency of random forest algorithms.
method Strengthened proof methods and propose DMRF algorithm.
result DMRF achieves better theoretical and experimental performance than previous variants.
Enhances random forests by smoothing predictions for better performance.
problem Suboptimal performance due to piecewise constant predictions in random forests.
method Kernel-based smoothing mechanism to introduce local regularity.
result Smoothed random forest model consistently improves predictive performance.
Random forests is a common non-parametric regression technique which performs well for mixed-type data and irrelevant covariates, while being robust to monotonic variable transformations. Existing random forest implementations target regression or classification. We introduce the RFCDE package for fitting random forest…
Random Planted Forest interprets tree-based models by keeping some splits, leading to more interpretable predictions.
problem Estimating the unknown regression function from lower-order interaction terms.
method Modifying the random forest algorithm by keeping certain leaves instead of deleting them, resulting in non-binary trees called planted trees.
result The random planted forest achieves asymptotically optimal convergence rates up to a logarithmic factor when the interaction bound is low.
Time series quantile regression using GRF for more accurate volatility estimation.
problem Estimating conditional quantiles for time series data accurately.
method Generalized Random Forests (GRF) for quantile regression on time series data.
result The tsQRF estimator is consistent under time series data assumptions.
Despite widespread interest and practical use, the theoretical properties of random forests are still not well understood. In this paper we contribute to this understanding in two ways. We present a new theoretically tractable variant of random regression forests and prove that our algorithm is consistent. We also prov…
Projected random forests improve circular data prediction with adaptive arc length and finite-sample coverage.
problem Regression with circular responses.
method Adapting linear-response models to circular data using projection and random forest out-of-bag mechanism.
result Projected random forest out-of-bag conformal prediction sets are more efficient and shorter than alternative methods.
E2Tree explains random forest models in regression tasks.
problem Lack of transparency in random forest models.
method E2Tree extends random forest to regression by explaining model predictions through graphical representation and dissimilarity measures.
result E2Tree provides a transparent explanation of random forest models in regression tasks.
A random forest is a popular tool for estimating probabilities in machine learning classification tasks. However, the means by which this is accomplished is unprincipled: one simply counts the fraction of trees in a forest that vote for a certain class. In this paper, we forge a connection between random forests and ke…
Approximates large Random Forest models to save space.
problem Large model size limits deployment on small devices.
method Approximate each tree using multinomial logistic regression.
result Reduced model size without significant loss of accuracy.
ABRF uses attention weights to improve RF performance.
problem Improving the performance of random forest models.
method Attention mechanism applied to random forest with optimization and gradient-based methods.
result The proposed ABRF models outperform standard RF models on various datasets.
GCQRF predicts survival quantiles without linearity assumptions.
problem Survival analysis with right censoring and nonlinearity.
method Global Censored Quantile Random Forest (GCQRF) for complex relationships.
result GCQRF outperforms existing methods in predictive accuracy.
New random forest variants achieve optimal performance in high dimensions.
problem Handling dependencies between features in high-dimensional data.
method Using oblique splits in random forests with general split directions.
result Achieved minimax optimal convergence rates in arbitrary dimension.
A new random forest algorithm improves tree construction for optimal performance.
problem Improving the performance of random forests, especially in complex and smooth scenarios.
method Adaptive split-balancing method using permutation-based splitting criterion.
result Achieves minimax optimality under various Lipschitz and Hölder classes.
Paper assesses error estimates of Random Forests classification.
problem Quantitative assessment of Random Forests error estimates.
method Theoretical and empirical investigation of various error estimation methods.
result Random Forests' error estimates are closer to true error rate than average prediction error.
Given an ensemble of randomized regression trees, it is possible to restructure them as a collection of multilayered neural networks with particular connection weights. Following this principle, we reformulate the random forest method of Breiman (2001) into a neural network setting, and in turn propose two new hybrid p…
We characterize and study variable importance (VIMP) and pairwise variable associations in binary regression trees. A key component involves the node mean squared error for a quantity we refer to as a maximal subtree. The theory naturally extends from single trees to ensembles of trees and applies to methods like rando…
Due to their accuracies, methods based on ensembles of regression trees are a popular approach for making predictions. Some common examples include Bayesian additive regression trees, boosting and random forests. This paper focuses on honest random forests, which add honesty to the original form of random forests and a…
Optimal weighted random forests improve prediction accuracy.
problem Unequal prediction performance among random forest trees.
method Proposes 1-step and 2-step optimal weighting algorithms.
result Asymptotically optimal in terms of squared loss and risk.
Paper proves a Central Limit Theorem for Random Forest Permutation Importance Measure.
problem Lack of theoretical analysis of Random Forest Permutation Importance Measure (RFPIM).
method Formal proof using U-Statistics theory, deviating from conventional Random Forest model.
result Established a Central Limit Theorem for RFPIM.
Alpha-trimming prunes trees in random forests to improve predictive performance.
problem Improving predictive performance of random forests by locally adaptive tree pruning.
method Alpha-trimming is a fast pruning algorithm that prunes trees in a random forest based on signal-to-noise ratio, controlled by a tuning parameter.
result Alpha-trimming often lowers mean squared prediction error compared to fully grown random forests.
A new random forest method for multivariate distributions.
problem Estimating complex multivariate distributions with heterogeneity.
method A novel splitting criterion based on MMD for multivariate responses.
result Estimates full conditional distribution for arbitrary targets.
This paper improves forest pruning to balance accuracy and interpretability.
problem Limited interpretability of regression forests.
method Lasso-pruning and theoretical analysis of regression forests.
result Pruned regression forests can achieve equal or better accuracy than unpruned ones, with significant size reduction.
Two new methods reduce random forest latency and improve accuracy.
problem High latency and memory demands in deep random forest models.
method DiNo and RanBu convert shallow random forests into efficient predictors.
result RanBu matches or exceeds full-depth random forest accuracy with up to 95% reduction in time.