coverforest 0.0.2

A template for scikit-learn compatible packages.

🌳 coverforest - Random Forest with Conformal Predictions

A simple and fast implementation of conformal random forests for both classification and regression tasks. coverforest extends scikit-learn's random forest implementation to provide prediction sets/intervals with guaranteed coverage using conformal prediction methods.

coverforest provides three conformal prediction methods for random forests:

• CV+ (Cross-Validation+) [1, 2].
• Jackknife+-after-Bootstrap [3].
• Split Conformal [4].

The library provides two main classes: CoverForestRegressor for interval prediction and CoverForestClassifier. for set prediction. Here are quick runs of the two classes:

from coverforest import CoverForestRegressor

reg = CoverForestRegressor(n_estimators=100, method='bootstrap')  # using J+-a-Bootstrap
reg.fit(X_train, y_train)
y_pred, y_intervals = reg.predict(X_test, alpha=0.05)             # 95% coverage intervals

from coverforest import CoverForestClassifier

clf = CoverForestClassifier(n_estimators=100, method='cv')  # using CV+
clf.fit(X_train, y_train)
y_pred, y_sets = clf.predict(X_test, alpha=0.05)            # 95% coverage sets

🔧 Requirements

• Python >=3.9
• Scikit-learn >=1.6.0

⚡ Installation

You can install coverforest using pip:

pip install coverforest

Or install from source:

git clone https://github.com/donlapark/coverforest.git
cd coverforest
pip install .

Regularization in conformal set predictions

The classifier includes two regularization parameters $k$ and $\lambda$ that encourage smaller prediction sets [5].

clf = CoverForestClassifier(n_estimators=100, method='cv', k_init=2, lambda_init=0.1)

Automatic searching for suitable $k$ and $\lambda$ is also possible by specifying k_init="auto" and lambda_init="auto", which are the default values of CoverForestClassifier.

Performance Tips

Random forest leverages parallel computation by processing trees concurrently. Use the n_jobs parameter in fit() and predict() to control CPU usage (n_jobs=-1 uses all cores).

For prediction, conformity score calculations require a memory array of size (n_train × n_test × n_classes). To optimize performance with high n_jobs values, split large test sets into smaller batches.

See the documentation for more details and examples.

🔗 See Also

• MAPIE: A Python package that provides scikit-learn-compatible wrappers for conformal classification and regression
• conforest An R implementation of random forest with inductive conformal prediction.
• clover A Python implementation of a regression forest method for conditional coverage ($P(Y \vert X =x)$) guarantee.
• Conformal Prediction: Jupyter Notebook demonstrations of conformal prediction on various tasks, such as image classification, image segmentation, times series forecasting, and outlier detection
• TorchCP A Python toolbox for Conformal Prediction in Deep Learning built on top of PyTorch
• crepes A Python package that implements standard and Mondrian conformal classifiers as well as standard, normalized and Mondrian conformal regressors and predictive systems.
• nonconformist: One of the first Python implementations of conformal prediction

📖 References

[1] Yaniv Romano, Matteo Sesia & Emmanuel J. Candès, "Classification with Valid and Adaptive Coverage", NeurIPS 2020.

[2] Rina Foygel Barber, Emmanuel J. Candès, Aaditya Ramdas & Ryan J. Tibshirani, "Predictive inference with the jackknife+", Ann. Statist. 49 (1) 486-507, 2021.

[3] Byol Kim, Chen Xu, Rina Foygel Barber, "Predictive inference is free with the jackknife+-after-bootstrap", NeurIPS 2020.

[4] Vladimir Vovk, Ilia Nouretdinov, Valery Manokhin & Alexander Gammerman, "Cross-conformal predictive distributions", 37-51, COPA 2018.

[5] Anastasios Nikolas Angelopoulos, Stephen Bates, Michael I. Jordan & Jitendra Malik, "Uncertainty Sets for Image Classifiers using Conformal Prediction", ICLR 2021.

[6] Leo Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.

📜 License

BSD-3-Clause license

📝 Citation

If you use coverforest in your research, please cite:

@software{coverforest2025,
  author = {Donlapark Ponnoprat},
  title = {coverforest: Fast Conformal Random Forests},
  year = {2025},
  publisher = {GitHub},
  url = {https://github.com/donlapark/coverforest}
}