{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/7"}],"enrichment":{"capability":"Computes confidence intervals and variance estimates for scikit-learn random forest regression and classification models, quantifying prediction uncertainty.","skillfed_tags":["uncertainty-quantification","scikit-learn-extension"],"use_cases":["Quantify prediction uncertainty in random forest regression to report confidence bands alongside point estimates.","Assess classification model confidence by computing error bars for predicted probabilities.","Diagnose training set sensitivity by examining how much predictions vary with different subsamples.","Validate model stability before deployment by checking whether confidence intervals narrow as tree count increases."],"what_it_does":"forestci extends scikit-learn's random forest objects by adding uncertainty quantification\u2014computing in-bag error estimates and confidence intervals around predictions. It wraps fitted forest models and calculates how much prediction variability stems from the training set composition, helping you understand how reliable individual predictions are.\n\nThe package is built on algorithms from Stefan Wager's R implementation and works with scikit-learn 1.0 through 1.9. It requires a large number of trees for reliable intervals; an optional calibration routine attempts to extrapolate results for infinite trees but can be unstable, so the description recommends disabling it with calibrate=False and increasing tree count if numerical errors occur.","worth_installing":"Yes. Active maintenance, permissive MIT license, low install friction, no known vulnerabilities, and a focused role in an underserved niche (uncertainty quantification for scikit-learn forests). Install if you need confidence intervals on random forest predictions; skip if you use other ensemble libraries or don't require uncertainty estimates."},"id":"forestci","links":{"html":"https://skillfed.io/packages/forestci","md":"https://skillfed.io/packages/forestci.md","pypi":"https://pypi.org/project/forestci/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-25","license_spdx":"MIT","license_treatment":"permissive","name":"forestci","python_support":"supports_current","summary":"Confidence intervals for scikit-learn forest algorithms"},"popularity":{"monthly_downloads":128512,"position":11704,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.8"}