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Found 15 papers, 7 papers with code
Trees, Forests, Chickens, and Eggs: When and Why to Prune Trees in a Random Forest
Due to their long-standing reputation as excellent off-the-shelf predictors, random forests continue remain a go-to model of choice for applied statisticians and data scientists.
Bridging Breiman's Brook: From Algorithmic Modeling to Statistical Learning
In 2001, Leo Breiman wrote of a divide between "data modeling" and "algorithmic modeling" cultures.
Posterior Calibrated Training on Sentence Classification Tasks
Here we propose an end-to-end training procedure called posterior calibrated (PosCal) training that directly optimizes the objective while minimizing the difference between the predicted and empirical posterior probabilities. We show that PosCal not only helps reduce the calibration error but also improve task performance by penalizing drops in performance of both objectives.
Getting Better from Worse: Augmented Bagging and a Cautionary Tale of Variable Importance
As the size, complexity, and availability of data continues to grow, scientists are increasingly relying upon black-box learning algorithms that can often provide accurate predictions with minimal a priori model specifications.
$V$-statistics and Variance Estimation
This paper develops a general framework for analyzing asymptotics of $V$-statistics.
Randomization as Regularization: A Degrees of Freedom Explanation for Random Forest Success
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.
Earlier Isn't Always Better: Sub-aspect Analysis on Corpus and System Biases in Summarization
We find that while position exhibits substantial bias in news articles, this is not the case, for example, with academic papers and meeting minutes.
Locally Optimized Random Forests
To estimate these ratios with an unlabeled test set, we make the covariate shift assumption, where the differences in distribution are only a function of the training distributions (Shimodaira, 2000.)
Asymptotic Distributions and Rates of Convergence for Random Forests via Generalized U-statistics
Random forests remain among the most popular off-the-shelf supervised learning algorithms.
Unrestricted Permutation forces Extrapolation: Variable Importance Requires at least One More Model, or There Is No Free Variable Importance
This paper reviews and advocates against the use of permute-and-predict (PaP) methods for interpreting black box functions.
Scalable and Efficient Hypothesis Testing with Random Forests
Throughout the last decade, random forests have established themselves as among the most accurate and popular supervised learning methods.
Multiphase Segmentation For Simultaneously Homogeneous and Textural Images
Segmentation remains an important problem in image processing.
Bootstrap Bias Corrections for Ensemble Methods
This paper examines the use of a residual bootstrap for bias correction in machine learning regression methods.
Formal Hypothesis Tests for Additive Structure in Random Forests
While statistical learning methods have proved powerful tools for predictive modeling, the black-box nature of the models they produce can severely limit their interpretability and the ability to conduct formal inference.
Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests
Instead of aggregating full bootstrap samples, we consider predicting by averaging over trees built on subsamples of the training set and demonstrate that the resulting estimator takes the form of a U-statistic.
