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YDF (short for Yggdrasil Decision Forests) is a library for training, serving, evaluating and analyzing decision forest models such as Random Forest and Gradient Boosted Trees.
The Python port of Yggdrasil Decision is a light-weight wrapper around Yggdrasil Decision Forests. It allows direct, fast access to YDF's methods and it also offers advanced import / export, evaluation and inspection methods. While the package is called YDF, the wrapping code is sometimes lovingly called PYDF.
YDF is the successor of Tensorflow Decision Forests (TF-DF). TF-DF is still maintained, but new projects should choose YDF for improved performance, better model quality and more features.
To install YDF, in Python, simply grab the package from pip:
pip install ydf
For build instructions, see INSTALLATION.md.
import ydf
import pandas as pd
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")
model = ydf.GradientBoostedTreesLearner(label="income").train(train_ds)
print(model.evaluate(test_ds))
model.save("my_model")
loaded_model = ydf.load_model("my_model")
See the FAQ in the documentation.
FAQs
YDF (short for Yggdrasil Decision Forests) is a library for training, serving, evaluating and analyzing decision forest models such as Random Forest and Gradient Boosted Trees.
We found that ydf demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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Socket now supports Scala and Kotlin, bringing AI-powered threat detection to JVM projects with easy manifest generation and fast, accurate scans.
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