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A random forest guided tour
The random forest algorithm, proposed by L. Breiman in 2001, has been extremely
successful as a general-purpose classification and regression method. The approach, which …
successful as a general-purpose classification and regression method. The approach, which …
Towards a science of human-ai decision making: a survey of empirical studies
As AI systems demonstrate increasingly strong predictive performance, their adoption has
grown in numerous domains. However, in high-stakes domains such as criminal justice and …
grown in numerous domains. However, in high-stakes domains such as criminal justice and …
All models are wrong, but many are useful: Learning a variable's importance by studying an entire class of prediction models simultaneously
Variable importance (VI) tools describe how much covariates contribute to a prediction
model's accuracy. However, important variables for one well-performing model (for example …
model's accuracy. However, important variables for one well-performing model (for example …
Generalized random forests
Generalized random forests Page 1 The Annals of Statistics 2019, Vol. 47, No. 2, 1148–1178
https://doi.org/10.1214/18-AOS1709 © Institute of Mathematical Statistics, 2019 GENERALIZED …
https://doi.org/10.1214/18-AOS1709 © Institute of Mathematical Statistics, 2019 GENERALIZED …
Estimation and inference of heterogeneous treatment effects using random forests
Many scientific and engineering challenges—ranging from personalized medicine to
customized marketing recommendations—require an understanding of treatment effect …
customized marketing recommendations—require an understanding of treatment effect …
Local linear forests
Random forests are a powerful method for nonparametric regression, but are limited in their
ability to fit smooth signals. Taking the perspective of random forests as an adaptive kernel …
ability to fit smooth signals. Taking the perspective of random forests as an adaptive kernel …
Correlation and variable importance in random forests
B Gregorutti, B Michel, P Saint-Pierre - Statistics and Computing, 2017 - Springer
This paper is about variable selection with the random forests algorithm in presence of
correlated predictors. In high-dimensional regression or classification frameworks, variable …
correlated predictors. In high-dimensional regression or classification frameworks, variable …
Standard errors and confidence intervals for variable importance in random forest regression, classification, and survival
H Ishwaran, M Lu - Statistics in medicine, 2019 - Wiley Online Library
Random forests are a popular nonparametric tree ensemble procedure with broad
applications to data analysis. While its widespread popularity stems from its prediction …
applications to data analysis. While its widespread popularity stems from its prediction …
Consistency of random forests
Consistency of random forests Page 1 The Annals of Statistics 2015, Vol. 43, No. 4, 1716–1741
DOI: 10.1214/15-AOS1321 © Institute of Mathematical Statistics, 2015 CONSISTENCY OF …
DOI: 10.1214/15-AOS1321 © Institute of Mathematical Statistics, 2015 CONSISTENCY OF …
Machine-Learning-Driven Discovery of Mn4+-Doped Red-Emitting Fluorides with Short Excited-State Lifetime and High Efficiency for Mini Light-Emitting Diode …
H Ming, Y Zhou, MS Molokeev, C Zhang… - ACS Materials …, 2024 - ACS Publications
The discovery of high-efficiency Mn4+-activated fluoride red phosphors with short excited-
state lifetimes (ESLs) is urgent and crucial for high-quality, wide-color-gamut display …
state lifetimes (ESLs) is urgent and crucial for high-quality, wide-color-gamut display …