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*[CatBoost](https://github.com/catboost/catboost) - An open-source gradient boosting on decision trees library. <imgheight="20"src="img/sklearn_big.png"alt="sklearn"> <imgheight="20"src="img/gpu_big.png"alt="GPU accelerated">
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*[ThunderGBM](https://github.com/Xtra-Computing/thundergbm) - Fast GBDTs and Random Forests on GPUs. <imgheight="20"src="img/sklearn_big.png"alt="sklearn"> <imgheight="20"src="img/gpu_big.png"alt="GPU accelerated">
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*[NGBoost](https://github.com/stanfordmlgroup/ngboost) - Natural Gradient Boosting for Probabilistic Prediction.
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*[TensorFlow Decision Forests](https://github.com/tensorflow/decision-forests) - A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras. <imgheight="20"src="img/keras_big.png"alt="keras"> <imgheight="20"src="img/tf2_big.png"alt="TensorFlow">
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*[TensorFlow Decision Forests](https://github.com/tensorflow/decision-forests) - A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras. <imgheight="20"src="img/keras_big.png"alt="keras"> <imgheight="20"src="img/tf_big2.png"alt="TensorFlow">
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