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A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
| Date | Stars |
|---|---|
| 2026-07-24 | 664 |
| 2026-07-25 | 664 |
| 2026-07-28 | 664 |
| 2026-07-30 | 664 |
| 2026-08-16 | 668 |
| 2026-08-19 | 669 |
| 2026-08-24 | 670 |
| 2026-08-26 | 670 |
| 2026-09-02 | 671 |
| 2026-09-12 | 672 |
| 2026-09-13 | 673 |
| 2026-09-19 | 674 |
| 2026-09-20 | 674 |
Today
— stars today
This week
+1 stars this week
This month
+5 stars this month
Momentum
1.0
growth rate 0.15%/day
<p align="center">
<img src="documentation/public/docs/image/logo_v2.png" />
</p>
[](https://pypi.org/project/ydf/)
[](https://opensource.org/licenses/Apache-2.0)
[](https://ydf.readthedocs.io/)
[](https://ydf.readthedocs.io/en/latest/)
[](https://pepy.tech/project/ydf)
**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
[](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<AbstractLearner> 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
[decision-foExcerpt of 4,632 characters
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Mathieu Guillame-Bert · Switzerland
687
Richard Stotz · Google
612
Copybara Service · @google
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Google ML Automation · Google
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Jan
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5
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Ivo List
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Howard Chiam
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Jean-Baptiste Lespiau
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Emmanuel Ferdman · Israel
2
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Peter Hawkins · Google
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Matthew Soulanille · Google · United States
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Matej Aleksandrov · Google · Germany
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:3cfc3f8b87c68dc8, topic:distributed-computing
matched fp:3cfc3f8b87c68dc8, topic:tensorflow
matched fp:3cfc3f8b87c68dc8, topic:interpretability