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

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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

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README

        
# NGBoost: Natural Gradient Boosting for Probabilistic Prediction



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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 pip

pip 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 NGBRegressor

from 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)