Package: forestError 1.1.0

forestError: A Unified Framework for Random Forest Prediction Error Estimation

Estimates the conditional error distributions of random forest predictions and common parameters of those distributions, including conditional misclassification rates, conditional mean squared prediction errors, conditional biases, and conditional quantiles, by out-of-bag weighting of out-of-bag prediction errors as proposed by Lu and Hardin (2021). This package is compatible with several existing packages that implement random forests in R.

Authors:Benjamin Lu and Johanna Hardin

forestError_1.1.0.tar.gz
forestError_1.1.0.zip(r-4.7)forestError_1.1.0.zip(r-4.6)forestError_1.1.0.zip(r-4.5)
forestError_1.1.0.tgz(r-4.6-any)forestError_1.1.0.tgz(r-4.5-any)
forestError_1.1.0.tar.gz(r-4.7-any)forestError_1.1.0.tar.gz(r-4.6-any)
forestError_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
forestError/json (API)
NEWS

# Install 'forestError' in R:
install.packages('forestError', repos = c('https://benjilu.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/benjilu/foresterror/issues

On CRAN:

Conda:

inferenceintervalsmachine-learningmachinelearningpredictionrandom-forestrandomforeststatistics

4.69 score 27 stars 18 scripts 276 downloads 2 exports 8 dependencies

Last updated from:5d9c4b2d01. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK111
source / vignettesOK167
linux-release-x86_64OK123
macos-release-arm64OK151
macos-oldrel-arm64OK188
windows-develOK98
windows-releaseOK92
windows-oldrelOK67
wasm-releaseOK108

Exports:findOOBErrorsquantForestError

Dependencies:clidata.tablegluelifecyclemagrittrpurrrrlangvctrs