ngboost

library0.5.10pypypi✓ verified 84d ago

NGBoost is a library for probabilistic prediction via gradient boosting that estimates full probability distributions rather than point predictions. Current version is 0.5.10, supporting Python 3.9–3.14. It uses natural gradient boosting to learn parameters of a parametric distribution. Release cadence is periodic, with recent versions adding distribution support (Weibull, HalfNormal, Beta, etc.), numpy2 compatibility, and sklearn compatibility. Maintained by the Stanford ML Group.

ai-ml data

pip install ngboost

github ↗package ↗docs ↗

INSTALL

IMPORT

SIG · NGBOOST

ngboost

ai-mlpythonv0.5.10

Install

23.8s avg

Import

9458ms

Disk

529MB

Pass rate

5/ 10

Env Coverage5 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.5.10 · 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 23.8s · import 9.458s · 497MB

529MB installed

● package 529MB

Code

Verified usage

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

NGBRegressor

✓ from ngboost import NGBRegressor

NGBClassifier

✓ from ngboost import NGBClassifier

✗ from ngboost.ngboost import NGBClassifier

Common error: trying to import from submodule instead of top-level

Normal

✓ from ngboost.distns import Normal

✗ from ngboost import Normal

Distributions are in ngboost.distns, not top-level.

LogScore

✓ from ngboost.scores import LogScore

✗ from ngboost import LogScore

Scores are in ngboost.scores submodule.

Basic usage of NGBoost for regression with a Normal distribution and LogScore.

import numpy as np from ngboost import NGBRegressor from ngboost.distns import Normal from ngboost.scores import LogScore # Generate sample data np.random.seed(42) X = np.random.randn(100, 1) y = X[:, 0] + 0.1*np.random.randn(100) # Train probabilistic model ngb = NGBRegressor(Dist=Normal, Score=LogScore, n_estimators=100, learning_rate=0.01, verbose=False) ngb.fit(X, y) # Predict: mean and variance (or scale) of Normal distribution y_preds = ngb.predict(X) # Use ngb.pred_dist(X) to get distribution object

Debug

Known issues

breakingVersion 0.5.5 upgraded sklearn dependency to >1.6. If using older sklearn, upgrade or pin ngboost<0.5.5.

fix

Update scikit-learn to >=1.6 or use ngboost<0.5.5

affects: 0.5.5+

breakingVersion 0.5.2 added support for Numpy 2.0. Code relying on numpy<2 behavior (e.g., object dtype in arrays) may break with ngboost>=0.5.2.

fix

Pin numpy<2 if using older scikit-learn or custom code that is not numpy2-compatible.

affects: 0.5.2+

deprecatedThe 'natural_gradient' parameter in some internal methods was deprecated in v0.5.8 and may be removed in future.

fix

Avoid using 'natural_gradient' parameter; default behavior is correct.

affects: 0.5.8+

gotchaDefault distribution is Normal with LogScore. If you use a different distribution, ensure the Score matches the distribution (e.g., LogScore for exponential family). Mismatch leads to silent poor performance.

fix

Always specify both Dist and Score parameters explicitly.

affects: all

gotchaModels saved with sklearn <1.3 may not load with sklearn >=1.3 due to pickle format. ngboost>=0.5.10 includes a compatibility loader `load_ngboost_model`.

fix

Use `from ngboost import load_ngboost_model` and call it instead of joblib.load.

affects: >=0.5.10

Errors

Common errors & fixes

ImportError: cannot import name 'NGBRegressor' from 'ngboost'

Incorrect installation or trying to import from a different environment.

fix

Run `pip install ngboost` and ensure you are in the correct Python environment. Then use `from ngboost import NGBRegressor`.

ModuleNotFoundError: No module named 'sklearn.metrics._pairwise_distances'

Incompatible scikit-learn version (likely <0.22) with newer ngboost.

fix

Upgrade scikit-learn: `pip install --upgrade scikit-learn`

AttributeError: 'NGBRegressor' object has no attribute 'pred_dist'

Using an older version of ngboost where the method is called `predict_dist` instead of `pred_dist`.

fix

Use `ngb.pred_dist(X)` for ngboost>=0.5.0; for older versions use `ngb.predict_dist(X)`.

ValueError: When `natural_gradient` is True, `Score` must be an instance of `LogScore` or a class that implements `natural_gradient`.

Attempting to use natural_gradient with a custom Score that doesn't support it.

fix

Either set `natural_gradient=False` or use a built-in Score like LogScore.

Upgrade

Version history

0.5.10latest on PyPI · released Mar 24, 2026

Audit

Dependencies

scikit-learnrequiredCore dependency for all models; version compatibility critical.

numpyrequiredCore dependency for arrays and numeric operations.

scipyrequiredUsed for distributions and optimization.

pandasoptionalOptional for example usage; not required for core.

Agent activity

32 hits · last 30 days

node

Resources

githubhttps://github.com/stanfordmlgroup/ngboost ↗

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

docshttps://stanfordmlgroup.github.io/ngboost/intro ↗