Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/tensorflow/decision-forests
A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
https://github.com/tensorflow/decision-forests
decision-forest decision-trees gradient-boosting interpretability keras machine-learning ml python random-forest tensorflow
Last synced: 3 days ago
JSON representation
A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
- Host: GitHub
- URL: https://github.com/tensorflow/decision-forests
- Owner: tensorflow
- License: apache-2.0
- Created: 2021-04-22T14:37:50.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-11-20T13:14:41.000Z (about 1 month ago)
- Last Synced: 2024-12-11T16:22:04.634Z (10 days ago)
- Topics: decision-forest, decision-trees, gradient-boosting, interpretability, keras, machine-learning, ml, python, random-forest, tensorflow
- Language: Python
- Homepage:
- Size: 5.71 MB
- Stars: 666
- Watchers: 24
- Forks: 110
- Open Issues: 40
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
- StarryDivineSky - tensorflow/decision-forests
README
**TensorFlow Decision Forests** (**TF-DF**) is a library to train, run and
interpret [decision forest](https://ydf.readthedocs.io/en/latest/intro_df.html)
models (e.g., Random Forests, Gradient Boosted Trees) in TensorFlow. TF-DF
supports classification, regression and ranking.
**TF-DF** is powered by
[Yggdrasil Decision Forest](https://github.com/google/yggdrasil-decision-forests)
(**YDF**, a library to train and use decision forests in C++, JavaScript, CLI,
and Go. TF-DF models are
[compatible](https://ydf.readthedocs.io/en/latest/convert_model.html#convert-a-a-tensorflow-decision-forests-model-to-a-yggdrasil-model)
with YDF' models, and vice versa.
Tensorflow Decision Forests is available on Linux and Mac. Windows users can use
the library through WSL+Linux.
## Usage example
A minimal end-to-end run looks as follows:
```python
import tensorflow_decision_forests as tfdf
import pandas as pd
# Load the dataset in a Pandas dataframe.
train_df = pd.read_csv("project/train.csv")
test_df = pd.read_csv("project/test.csv")
# Convert the dataset into a TensorFlow dataset.
train_ds = tfdf.keras.pd_dataframe_to_tf_dataset(train_df, label="my_label")
test_ds = tfdf.keras.pd_dataframe_to_tf_dataset(test_df, label="my_label")
# Train the model
model = tfdf.keras.RandomForestModel()
model.fit(train_ds)
# Look at the model.
model.summary()
# Evaluate the model.
model.evaluate(test_ds)
# Export to a TensorFlow SavedModel.
# Note: the model is compatible with Yggdrasil Decision Forests.
model.save("project/model")
```
## Google I/O Presentation
## Documentation & Resources
The following resources are available:
- [TF-DF on TensorFlow.org](https://tensorflow.org/decision_forests) (API
Reference, Guides and Tutorials)
- [Tutorials](https://www.tensorflow.org/decision_forests/tutorials) (on
tensorflow.org)
- [YDF documentation](https://ydf.readthedocs.io) (also applicable to TF-DF)
- [Issue tracker](https://github.com/tensorflow/decision-forests/issues)
- [Known issues](documentation/known_issues.md)
- [Changelog](CHANGELOG.md)
- [More examples](documentation/more_examples.md)
## Installation
To install TensorFlow Decision Forests, run:
```shell
pip3 install tensorflow_decision_forests --upgrade
```
See the [installation](documentation/installation.md) page for more details,
troubleshooting and alternative installation solutions.
## Contributing
Contributions to TensorFlow Decision Forests and Yggdrasil Decision Forests are
welcome. If you want to contribute, make sure to review the
[developer manual](documentation/developer_manual.md) and
[contribution guidelines](CONTRIBUTING.md).
## Citation
If you us Tensorflow Decision Forests in a scientific publication, please cite
the following paper:
[Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library](https://doi.org/10.1145/3580305.3599933).
**Bibtex**
```
@inproceedings{GBBSP23,
author = {Mathieu Guillame{-}Bert and
Sebastian Bruch and
Richard Stotz and
Jan Pfeifer},
title = {Yggdrasil Decision Forests: {A} Fast and Extensible Decision Forests
Library},
booktitle = {Proceedings of the 29th {ACM} {SIGKDD} Conference on Knowledge Discovery
and Data Mining, {KDD} 2023, Long Beach, CA, USA, August 6-10, 2023},
pages = {4068--4077},
year = {2023},
url = {https://doi.org/10.1145/3580305.3599933},
doi = {10.1145/3580305.3599933},
}
```
**Raw**
Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library,
Guillame-Bert et al., KDD 2023: 4068-4077. doi:10.1145/3580305.3599933
## Contact
You can contact the core development team at
[[email protected]](mailto:[email protected]).
## Credits
TensorFlow Decision Forests was developed by:
- Mathieu Guillame-Bert (gbm AT google DOT com)
- Jan Pfeifer (janpf AT google DOT com)
- Richard Stotz (richardstotz AT google DOT com)
- Sebastian Bruch (sebastian AT bruch DOT io)
- Arvind Srinivasan (arvnd AT google DOT com)
## License
[Apache License 2.0](LICENSE)