IBM SPSS Statistics From $79/mo 9.3 Visit forestci
contrib.scikit-learn.org
Spec sheet
- Purpose
- forestci adds variance calculations and confidence intervals to scikit-learn random forest regression and classification objects.Read 12 Oct 2026 · contrib.scikit-learn.org
- Free plan
- YesRead 23 Sep 2026 · contrib.scikit-learn.org
- API
- The documented `random_forest_error` function calculates error bars from scikit-learn RandomForest estimators.Read 12 Oct 2026 · contrib.scikit-learn.org
- Support
- The repository directs software issue reports to its issue log and lists email contacts for questions, suggestions, or feedback.Read 12 Oct 2026 · github.com
- License
- The repository identifies the software as licensed under the MIT open-source license.Read 12 Oct 2026 · github.com
- Bayesian analysis
- NoRead 23 Sep 2026 · contrib.scikit-learn.org
- Calibration
- The optional calibration routine attempts to extrapolate results for an infinite number of trees and may be unstable or cause numerical errors.Read 12 Oct 2026 · github.com
- Calibration limitation
- The documentation says calibration may worsen agreement with an empirical reference in some cases and is not universally recommended.Read 11 Oct 2026 · contrib.scikit-learn.org
- Compatibility
- The repository states that version 0.9 supports scikit-learn 1.0 through 1.9.Read 12 Oct 2026 · github.com
- Contribution
- The project welcomes contributions through pull requests and recommends discussing large changes in an issue first.Read 12 Oct 2026 · contrib.scikit-learn.org
- Contributions
- The documentation welcomes suggestions and contributions through pull requests and recommends discussing large changes in an issue first.Read 11 Oct 2026 · contrib.scikit-learn.org
- Core function
- The random_forest_error function calculates error bars from scikit-learn RandomForest estimators.Read 11 Oct 2026 · contrib.scikit-learn.org
- Dependencies
- The repository says users need NumPy, SciPy, and scikit-learn before installing forestci.Read 12 Oct 2026 · github.com
- Examples
- Examples demonstrate error bars for random forest regressors and classifiers using standard machine learning datasets.Read 12 Oct 2026 · contrib.scikit-learn.org
- Install
- The installation guide gives `python -m pip install forestci` as the install command.Read 12 Oct 2026 · contrib.scikit-learn.org
- Installation
- The installation guide gives `python -m pip install forestci` as the package installation command.Read 11 Oct 2026 · contrib.scikit-learn.org
- Integration
- Version 0.9 supports scikit-learn versions 1.0 through 1.9.Read 11 Oct 2026 · github.com
- Method
- The package implements an algorithm developed by Wager and colleagues.Read 12 Oct 2026 · contrib.scikit-learn.org
- Regression models
- YesRead 23 Sep 2026 · contrib.scikit-learn.org
- Scripting support
- YesRead 23 Sep 2026 · contrib.scikit-learn.org
- Statistical tests
- NoRead 23 Sep 2026 · contrib.scikit-learn.org
- Supported models
- The documentation describes confidence intervals for RandomForestRegressor and RandomForestClassifier objects.Read 11 Oct 2026 · contrib.scikit-learn.org
- Tree count
- The documentation says a large number of trees is needed to obtain proper confidence intervals.Read 12 Oct 2026 · github.com
Plans and prices
forestci Free Open-source Python package · MIT license Read 12 Oct 2026 · github.com
Where Specifiction places it
- Statistical Analysis Software#41 of 1068.0
