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# Install TF-DF
!pip install tensorflow tensorflow_decision_forests

# Load TF-DF
import tensorflow_decision_forests as tfdf
import pandas as pd

# Load a 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 a Random Forest model.
model = tfdf.keras.RandomForestModel()
model.fit(train_ds)

# Summary of the model structure.
model.summary()

# Evaluate the model.
model.evaluate(test_ds)

# Export the model to a SavedModel.
model.save("project/model")

TensorFlow Decision Forests (TF-DF) is a library to train, run and interpret decision forest models (e.g., Random Forests, Gradient Boosted Trees) in TensorFlow. TF-DF supports classification, regression, ranking and uplifting. It is available on Linux and Mac. Window users can use WSL+Linux.

TF-DF is powered by Yggdrasil Decision Forest (YDF), a library to train and use decision forests in c++, JavaScript, CLI, and Go. TF-DF models are compatible with YDF models, and vice versa.

Keywords: Decision Forests, TensorFlow, Random Forest, Gradient Boosted Trees, CART, model interpretation.

Documentation & Resources

The following resources are available:

Community

  • TensorFlow forum
  • TensorFlow Decision Forest on Github
  • Yggdrasil Decision Forest on Github
  • Contributing

    Contributions to TensorFlow Decision Forests and Yggdrasil Decision Forests are welcome. If you want to contribute, make sure to review the developer manual.