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Author: Mathieu Guillame-Bert, Richard Stotz, Jan Pfeifer

Requires Python: >=3.9

Classifiers

Intended Audience

Developers
Education
Science/Research

License

OSI Approved :: Apache Software License

Programming Language

Python :: 3
Python :: 3.9
Python :: 3.10
Python :: 3.11
Python :: 3.12
Python :: 3.13
Python :: 3 :: Only

Topic

Scientific/Engineering
Scientific/Engineering :: Mathematics
Scientific/Engineering :: Artificial Intelligence
Software Development
Software Development :: Libraries
Software Development :: Libraries :: Python Modules

YDF - Yggdrasil Decision Forests for Python

YDF is a library for training, serving, and interpreting decision forest models. It acts as a lightweight, efficient wrapper around the C++ Yggdrasil Decision Forests library.

It provides fast access to core methods along with advanced features for model import/export, evaluation, and inspection.

YDF is the official successor to TensorFlow Decision Forests (TF-DF) and is recommended for new projects due to its superior performance and features.

Installation

Install YDF from PyPI:

pip install ydf

For detailed build instructions, see INSTALLATION.md.

Usage Example

import ydf
import pandas as pd

# Load dataset
ds_path = "https://raw.githubusercontent.com/google/yggdrasil-decision-forests/main/yggdrasil_decision_forests/test_data/dataset"
train_ds = pd.read_csv(f"{ds_path}/adult_train.csv")
test_ds = pd.read_csv(f"{ds_path}/adult_test.csv")

# Train a Gradient Boosted Trees model
model = ydf.GradientBoostedTreesLearner(label="income").train(train_ds)

# Evaluate the model
print(model.evaluate(test_ds))

# Save the model
model.save("my_model")

# Load the model
loaded_model = ydf.load_model("my_model")