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- forestci
On the recordRead 12 Oct 202623 facts
forestci
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forestci
Free
Open-source Python package · MIT license
Read 12 Oct 2026 · github.com
On the record
- API
- The documented `random_forest_error` function calculates error bars from scikit-learn RandomForest estimators.contrib.scikit-learn.org · read 12 Oct 2026
- Bayesian analysis
- Nocontrib.scikit-learn.org · read 23 Sep 2026
- Calibration
- The optional calibration routine attempts to extrapolate results for an infinite number of trees and may be unstable or cause numerical errors.github.com · read 12 Oct 2026
- Calibration limitation
- The documentation says calibration may worsen agreement with an empirical reference in some cases and is not universally recommended.contrib.scikit-learn.org · read 11 Oct 2026
- Compatibility
- The repository states that version 0.9 supports scikit-learn 1.0 through 1.9.github.com · read 12 Oct 2026
- Contribution
- The project welcomes contributions through pull requests and recommends discussing large changes in an issue first.contrib.scikit-learn.org · read 12 Oct 2026
- Contributions
- The documentation welcomes suggestions and contributions through pull requests and recommends discussing large changes in an issue first.contrib.scikit-learn.org · read 11 Oct 2026
- Core function
- The random_forest_error function calculates error bars from scikit-learn RandomForest estimators.contrib.scikit-learn.org · read 11 Oct 2026
- Dependencies
- The repository says users need NumPy, SciPy, and scikit-learn before installing forestci.github.com · read 12 Oct 2026
- Examples
- Examples demonstrate error bars for random forest regressors and classifiers using standard machine learning datasets.contrib.scikit-learn.org · read 12 Oct 2026
- Install
- The installation guide gives `python -m pip install forestci` as the install command.contrib.scikit-learn.org · read 12 Oct 2026
- Installation
- The installation guide gives `python -m pip install forestci` as the package installation command.contrib.scikit-learn.org · read 11 Oct 2026
- Integration
- Version 0.9 supports scikit-learn versions 1.0 through 1.9.github.com · read 11 Oct 2026
- License
- The repository identifies the software as licensed under the MIT open-source license.github.com · read 12 Oct 2026
- Method
- The package implements an algorithm developed by Wager and colleagues.contrib.scikit-learn.org · read 12 Oct 2026
- Purpose
- forestci adds variance calculations and confidence intervals to scikit-learn random forest regression and classification objects.contrib.scikit-learn.org · read 12 Oct 2026
- Regression models
- Yescontrib.scikit-learn.org · read 23 Sep 2026
- Scripting support
- Yescontrib.scikit-learn.org · read 23 Sep 2026
