forestci

library0.7pypypiunverified

forestci provides confidence intervals for random forest predictions using the infinitesimal jackknife method. It supports scikit-learn's RandomForestClassifier, RandomForestRegressor, ExtraTreesClassifier, and ExtraTreesRegressor. Version 0.7 includes bug fixes and improved documentation, with no breaking changes from 0.6. The library is stable but released infrequently.

ai-ml

pip install forestci

github ↗package ↗

INSTALL

IMPORT

SIG · FORESTCI

forestci

ai-mlpythonv0.7

Install

10.6s avg

Import

—

Disk

281MB

Pass rate

5/ 10

Env Coverage5 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.7 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

build_error

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 10.6s · import 0.000s · 271MB

281MB installed

● package 281MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

calc_inference

✓ from forestci import calc_inference

main function for computing confidence intervals

random_forest_error

✓ from forestci import random_forest_error

computes bias-corrected error variance

forestci

✓ import forestci

✗ from forestci import forestci

forestci is a module, not a function; do not import as a submodule

Compute confidence intervals for RandomForestRegressor predictions using the infinitesimal jackknife.

from sklearn.ensemble import RandomForestRegressor from sklearn.datasets import make_regression from forestci import random_forest_error import numpy as np X, y = make_regression(n_samples=100, n_features=4, noise=0.1, random_state=42) rf = RandomForestRegressor(n_estimators=100, random_state=42) rf.fit(X, y) pred = rf.predict(X) error_var = random_forest_error(rf, X, X) # 95% confidence interval ci = 1.96 * np.sqrt(error_var) print(ci[:5])

Debug

Known issues

gotcharandom_forest_error requires the training data (X_train) to compute the inbag matrix. If you pass the same data for prediction, ensure it is the training set.

fix

Use X_train as the 'inbag' argument or pass the training data explicitly.

affects: all

gotchacalc_inference (formerly calc_corr) has been deprecated. Use random_forest_error for variance estimation and then compute confidence intervals manually.

fix

Replace calc_inference with random_forest_error(
    rf, X_test, X_train
) + manual CI computation.

affects: >=0.6

gotchaIn version 0.7, the random_forest_error function may raise AttributeError if the forest estimator doesn't have estimators_samples_ attribute (e.g., older sklearn versions).

fix

Upgrade scikit-learn to 0.24+ or use the forestci compatible version. Check estimator type.

affects: 0.7

Upgrade

Version history

0.7latest on PyPI · released Jul 9, 2024

Audit

Dependencies

scikit-learnrequiredforestci works with scikit-learn's forest estimators; requires sklearn >= 0.21

numpyrequiredused for array operations

scipyrequiredused for statistical functions

Agent activity

13 hits · last 30 days

node

OpenAI (training)

Resources

githubhttp://github.com/scikit-learn-contrib/forest-confidence-interval ↗

packagehttps://pypi.org/project/forestci/ ↗