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A collection of state-of-the-art Decision Forest algorithms for regression, classification, and ranking applications in TensorFlow.

TensorFlow Decision Forests (TF-DF) is a library for training, running, and interpreting decision forest models within the TensorFlow ecosystem. It supports various machine learning tasks including classification, regression, ranking, and uplift modeling. TF-DF offers implementations of Random Forests and Gradient Boosted Trees, allowing users to leverage these algorithms for tabular data analysis. The library provides tools for model interpretation, helping users understand the behavior and predictions of their models. YDF (Yggdrasil Decision Forests) extends TF-DF with new features, a simplified API, and faster training times. TF-DF integrates seamlessly with TensorFlow, enabling users to deploy decision forest models in TensorFlow Serving and other TensorFlow-based environments. It natively handles numeric, categorical, and missing features, reducing the need for extensive preprocessing.
TensorFlow Decision Forests (TF-DF) is a library for training, running, and interpreting decision forest models within the TensorFlow ecosystem.
Explore all tools that specialize in uplift modeling. This domain focus ensures TensorFlow Decision Forests delivers optimized results for this specific requirement.
Implements the Random Forest algorithm for classification and regression tasks, improving prediction accuracy by aggregating multiple decision trees.
Utilizes the Gradient Boosting algorithm to sequentially train decision trees, reducing bias and improving model performance.
Offers tools for visualizing and understanding the decision-making process of decision forest models, including feature importance and partial dependence plots.
Provides integration with YDF, Google's new library for training decision forests, offering faster training times and improved performance.
Natively handles numeric, categorical, and missing features, reducing the need for extensive preprocessing and feature engineering.
Install TensorFlow Decision Forests: `pip install tensorflow tensorflow_decision_forests`
Import the library: `import tensorflow_decision_forests as tfdf`
Load your dataset into a Pandas DataFrame: `train_df = pd.read_csv("project/train.csv")`
Convert the Pandas DataFrame to a TensorFlow Dataset: `train_ds = tfdf.keras.pd_dataframe_to_tf_dataset(train_df, label="my_label")`
Train a Random Forest model: `model = tfdf.keras.RandomForestModel(); model.fit(train_ds)`
Evaluate model accuracy: `model.compile(metrics=["accuracy"]); model.evaluate(test_ds, return_dict=True)`
Export the model to a SavedModel format: `model.save("project/model")`
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"Generally praised for its integration with TensorFlow and strong performance, but can be complex for beginners."
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