NGBoost: Natural Gradient Boosting for Probabilistic Prediction
ngboost is a Python library that implements Natural Gradient Boosting, as described in "NGBoost: Natural Gradient Boosting for Probabilistic Prediction". It is built on top of Scikit-Learn, and is designed to be scalable and modular with respect to choice of proper scoring rule, distribution, and base learner. A didactic introduction to the methodology underlying NGBoost is available in this slide deck.
Installation
via pip
pip install --upgrade ngboost
via conda-forge
conda install -c conda-forge ngboost
Usage
Probabilistic regression example on the Boston housing dataset:
from ngboost import NGBRegressor
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
# Load California housing dataset
cal = fetch_california_housing()
X, Y = cal.data, cal.target
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)
ngb = NGBRegressor().fit(X_train, Y_train)
Y_preds = ngb.predict(X_test)
Y_dists = ngb.pred_dist(X_test)
# test Mean Squared Error
test_MSE = mean_squared_error(Y_preds, Y_test)
print('Test MSE', test_MSE)
# test Negative Log Likelihood
test_NLL = -Y_dists.logpdf(Y_test).mean()
print('Test NLL', test_NLL)
Details on available distributions, scoring rules, learners, tuning, and model interpretation are available in our user guide, which also includes numerous usage examples and information on how to add new distributions or scores to NGBoost.
License
Reference
Tony Duan, Anand Avati, Daisy Yi Ding, Khanh K. Thai, Sanjay Basu, Andrew Y. Ng, Alejandro Schuler. 2019. NGBoost: Natural Gradient Boosting for Probabilistic Prediction. arXiv
2 maintainers
Metadata
2 maintainers
Release files for ngboost 0.5.11
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| ngboost-0.5.11.tar.gz | 42.4 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| ngboost-0.5.11-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 95.4 kB
Release files / ngboost-0.5.11.tar.gz
| Download URL | ngboost-0.5.11.tar.gz |
|---|---|
| Size | 42.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
873326442f00632829a521209b1030e150e0ef0a9de09952e044f8f3b8839940
|
|
BLAKE2b-256 checksum How to use checksums |
6866119fe55e3b0ab091101778b9b7d228ca27b75e5da88c0424f93594a2f236
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
poetry/2.0.1 CPython/3.13.2 Darwin/25.6.0
|
Release files / ngboost-0.5.11-py3-none-any.whl
| Download URL | ngboost-0.5.11-py3-none-any.whl |
|---|---|
| Size | 53.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
c3683334ab6ad58d79bc50aa11d8f090f4d452010c73dc2f2304affc448b08fa
|
|
BLAKE2b-256 checksum How to use checksums |
52f6c3ede5cc357b2c1c70d626c68ece0c352bac5d5314652b4ad35215ed6bc8
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
poetry/2.0.1 CPython/3.13.2 Darwin/25.6.0
|
Security for ngboost 0.5.11
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
