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https://github.com/google/yggdrasil-decision-forests

A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
https://github.com/google/yggdrasil-decision-forests

cart cli cpp decision-forest decision-trees distributed-computing go gradient-boosting interpretability javascript machine-learning ml pypi python random-forest tensorflow

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A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.

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README 

        







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**YDF** (Yggdrasil Decision Forests) is a library to train, evaluate, interpret,

and serve Random Forest, Gradient Boosted Decision Trees, CART and Isolation

forest models.



See the [documentation](https://ydf.readthedocs.org/) for more information on

YDF.



## Installation



To install YDF from [PyPI](https://pypi.org/project/ydf/), run:



```shell

pip install ydf -U

```



## Usage example



[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google/yggdrasil-decision-forests/blob/main/documentation/public/docs/tutorial/usage_example.ipynb)



```python

import ydf

import pandas as pd



# Load dataset with Pandas

ds_path = "https://raw.githubusercontent.com/google/yggdrasil-decision-forests/main/yggdrasil_decision_forests/test_data/dataset/"

train_ds = pd.read_csv(ds_path + "adult_train.csv")

test_ds = pd.read_csv(ds_path + "adult_test.csv")



# Train a Gradient Boosted Trees model

model = ydf.GradientBoostedTreesLearner(label="income").train(train_ds)



# Look at a model (input features, training logs, structure, etc.)

model.describe()



# Evaluate a model (e.g. roc, accuracy, confusion matrix, confidence intervals)

model.evaluate(test_ds)



# Generate predictions

model.predict(test_ds)



# Analyse a model (e.g. partial dependence plot, variable importance)

model.analyze(test_ds)



# Benchmark the inference speed of a model

model.benchmark(test_ds)



# Save the model

model.save("/tmp/my_model")

```



Example with the C++ API.



```c++

auto dataset_path = "csv:train.csv";



// List columns in training dataset

DataSpecification spec;

CreateDataSpec(dataset_path, false, {}, &spec);



// Create a training configuration

TrainingConfig train_config;

train_config.set_learner("RANDOM_FOREST");

train_config.set_task(Task::CLASSIFICATION);

train_config.set_label("my_label");



// Train model

std::unique_ptr learner;

GetLearner(train_config, &learner);

auto model = learner->Train(dataset_path, spec);



// Export model

SaveModel("my_model", model.get());

```



(based on [examples/beginner.cc](examples/beginner.cc))



## Next steps



Check the

[Getting Started tutorial 🧭](https://ydf.readthedocs.io/en/stable/tutorial/getting_started/).



## Citation



If you us Yggdrasil 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



Yggdrasil Decision Forests and TensorFlow Decision Forests are developed by:



-   Mathieu Guillame-Bert (gbm AT google DOT com)

-   Jan Pfeifer (janpf AT google DOT com)

-   Sebastian Bruch (sebastian AT bruch DOT io)

-   Richard Stotz (richardstotz AT google DOT com)

-   Arvind Srinivasan (arvnd AT google DOT com)



## Contributing



Contributions to TensorFlow Decision Forests and Yggdrasil Decision Forests are

welcome. If you want to contribute, check the

[contribution guidelines](CONTRIBUTING.md).



## License



[Apache License 2.0](LICENSE)