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-any)forestError_1.1.0.zip(r-4.6-any)forestError_1.1.0.zip(r-4.5-any)
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✨
DESCRIPTION |NEWS
card.svg |card.png
forestError/json (API)

# 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 339 downloads 2 exports 8 dependencies

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

TargetResultTimeFilesSyslog
linux-develOK162
source / vignettesOK146
linux-releaseOK130
macos-releaseOK155
macos-oldrelOK140
windows-develOK61
windows-releaseOK58
windows-oldrelOK64
wasm-releaseOK98

Exports:findOOBErrorsquantForestError

Dependencies:clidata.tablegluelifecyclemagrittrpurrrrlangvctrs