forestci
The facts below are what the maker publishes about forestci, checked and dated. Our editors have not written about it yet.
Plans and prices
From forestci's own pricing page, checked 12 Oct 2026.
| Plan | Price | What it gives you |
|---|---|---|
| forestci | Free | Open-source Python package · MIT license |
The facts
Each one read from forestci's own pages, with the date it was checked.
What it does
- API
- The documented `random_forest_error` function calculates error bars from scikit-learn RandomForest estimators.checked 12 Oct 2026
- Bayesian analysis
- Nochecked 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.checked 12 Oct 2026
- Calibration limitation
- The documentation says calibration may worsen agreement with an empirical reference in some cases and is not universally recommended.checked 11 Oct 2026
- Compatibility
- The repository states that version 0.9 supports scikit-learn 1.0 through 1.9.checked 12 Oct 2026
- Contribution
- The project welcomes contributions through pull requests and recommends discussing large changes in an issue first.checked 12 Oct 2026
- Contributions
- The documentation welcomes suggestions and contributions through pull requests and recommends discussing large changes in an issue first.checked 11 Oct 2026
- Core function
- The random_forest_error function calculates error bars from scikit-learn RandomForest estimators.checked 11 Oct 2026
- Dependencies
- The repository says users need NumPy, SciPy, and scikit-learn before installing forestci.checked 12 Oct 2026
- Examples
- Examples demonstrate error bars for random forest regressors and classifiers using standard machine learning datasets.checked 12 Oct 2026
- Install
- The installation guide gives `python -m pip install forestci` as the install command.checked 12 Oct 2026
- Installation
- The installation guide gives `python -m pip install forestci` as the package installation command.checked 11 Oct 2026
- Integration
- Version 0.9 supports scikit-learn versions 1.0 through 1.9.checked 11 Oct 2026
- License
- The repository identifies the software as licensed under the MIT open-source license.checked 12 Oct 2026
- Method
- The package implements an algorithm developed by Wager and colleagues.checked 12 Oct 2026
- Purpose
- forestci adds variance calculations and confidence intervals to scikit-learn random forest regression and classification objects.checked 12 Oct 2026
- Regression models
- Yeschecked 23 Sep 2026
- Scripting support
- Yeschecked 23 Sep 2026
- Statistical tests
- Nochecked 23 Sep 2026
- Support
- The repository directs software issue reports to its issue log and lists email contacts for questions, suggestions, or feedback.checked 12 Oct 2026
- Supported models
- The documentation describes confidence intervals for RandomForestRegressor and RandomForestClassifier objects.checked 11 Oct 2026
- Tree count
- The documentation says a large number of trees is needed to obtain proper confidence intervals.checked 12 Oct 2026
Money
- Free plan
- Yeschecked 23 Sep 2026
