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https://github.com/stanfordmlgroup/ngboost
Natural Gradient Boosting for Probabilistic Prediction
https://github.com/stanfordmlgroup/ngboost
gradient-boosting machine-learning natural-gradients ngboost python uncertainty-estimation
Last synced: 3 months ago
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Natural Gradient Boosting for Probabilistic Prediction
- Host: GitHub
- URL: https://github.com/stanfordmlgroup/ngboost
- Owner: stanfordmlgroup
- License: apache-2.0
- Created: 2018-06-21T22:22:40.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-02-21T02:20:47.000Z (9 months ago)
- Last Synced: 2024-04-14T06:42:57.824Z (7 months ago)
- Topics: gradient-boosting, machine-learning, natural-gradients, ngboost, python, uncertainty-estimation
- Language: Python
- Homepage:
- Size: 12.2 MB
- Stars: 1,577
- Watchers: 44
- Forks: 219
- Open Issues: 45
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS
Awesome Lists containing this project
- awesome-gradient-boosting-papers - [Code
README
# NGBoost: Natural Gradient Boosting for Probabilistic Prediction
![Python package](https://github.com/stanfordmlgroup/ngboost/workflows/Python%20package/badge.svg)
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[![PyPI](https://img.shields.io/pypi/v/ngboost?logo=pypi&logoColor=white)](https://pypi.org/project/ngboost)
[![PyPI Downloads](https://img.shields.io/pypi/dm/ngboost?logo=icloud&logoColor=white)](https://pypistats.org/packages/ngboost)ngboost is a Python library that implements Natural Gradient Boosting, as described in ["NGBoost: Natural Gradient Boosting for Probabilistic Prediction"](https://stanfordmlgroup.github.io/projects/ngboost/). It is built on top of [Scikit-Learn](https://scikit-learn.org/stable/), 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](https://docs.google.com/presentation/d/1Tn23Su0ygR6z11jy3xVNiLGv0ggiUQue/edit?usp=share_link&ouid=102290675300480810195&rtpof=true&sd=true).
## Installation
```sh
via pippip install --upgrade ngboost
via conda-forge
conda install -c conda-forge ngboost
```## Usage
Probabilistic regression example on the Boston housing dataset:
```python
from ngboost import NGBRegressorfrom sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error#Load Boston housing dataset
data_url = "http://lib.stat.cmu.edu/datasets/boston"
raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None)
X = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
Y = raw_df.values[1::2, 2]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](https://stanfordmlgroup.github.io/ngboost/intro.html), which also includes numerous usage examples and information on how to add new distributions or scores to NGBoost.
## License
[Apache License 2.0](https://github.com/stanfordmlgroup/ngboost/blob/master/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](https://arxiv.org/abs/1910.03225)