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LightGBM is a gradient boosting classifier in machine learning that uses tree-based learning algorithms. It is designed to be distributed and efficient with faster drive speed and higher efficiency, lower memory usage and better accuracy.

Light GBM grows tree vertically while other algorithm grows trees horizontally meaning that Light GBM grows tree leaf-wise while other algorithm grows level-wise. It will choose the leaf with max delta loss to grow. When growing the same leaf, Leaf-wise algorithm can reduce more loss than a level-wise algorithm.

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Author of the review:
Spiros Sirmakessis
University of Peloponnese


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