Package: tensorflow-decision-forests
TensorFlow Decision Forests(TF-DF) is a library to train, run and
interpretdecision forestmodels (e.g., Random Forests, Gradient Boosted Trees) in TensorFlow. TF-DF
supports classification, regression and ranking.TF-DFis powered byYggdrasil Decision Forest(YDF, a library to train and use decision forests in C++, JavaScript, CLI,
and Go. TF-DF models arecompatiblewith YDF' models, and vice versa.Tensorflow Decision Forests is available on Linux and Mac. Windows users can use
the library through WSL+Linux.Usage exampleA minimal end-to-end run looks as follows:importtensorflow_decision_forestsastfdfimportpandasaspd# Load the 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 the modelmodel=tfdf.keras.RandomForestModel()model.fit(train_ds)# Look at the model.model.summary()# Evaluate the model.model.evaluate(test_ds)# Export to a TensorFlow SavedModel.# Note: the model is compatible with Yggdrasil Decision Forests.model.save("project/model")Google I/O PresentationDocumentation & ResourcesThe following resources are available:TF-DF on TensorFlow.org(API
Reference, Guides and Tutorials)Tutorials(on
tensorflow.org)YDF documentation(also applicable to TF-DF)Issue trackerKnown issuesChangelogTensorFlow Forum(on
discuss.tensorflow.org)More examplesInstallationTo install TensorFlow Decision Forests, run:pip3installtensorflow_decision_forests--upgradeSee theinstallationpage for more details,
troubleshooting and alternative installation solutions.ContributingContributions to TensorFlow Decision Forests and Yggdrasil Decision Forests are
welcome. If you want to contribute, make sure to review thedeveloper manualandcontribution guidelines.CitationIf you us Tensorflow Decision Forests in a scientific publication, please cite
the following paper:Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library.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},
}RawYggdrasil Decision Forests: A Fast and Extensible Decision Forests Library,
Guillame-Bert et al., KDD 2023: 4068-4077. doi:10.1145/3580305.3599933ContactYou can contact the core development team atdecision-forests-contact@google.com.CreditsTensorFlow Decision Forests was developed by:Mathieu Guillame-Bert (gbm AT google DOT com)Jan Pfeifer (janpf AT google DOT com)Richard Stotz (richardstotz AT google DOT com)Sebastian Bruch (sebastian AT bruch DOT io)Arvind Srinivasan (arvnd AT google DOT com)LicenseApache License 2.0