Lightgbm parameters explained

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Lightgbm parameters explained

Lightgbm parameters explained

Many boosting tools use pre-sort-based algorithms [2, 3] (e.g. default algorithm in xgboost) for decision tree learning. It is a simple solution, but not easy to optimize. LightGBM uses histogram-based algorithms [4, 5, 6], which bucket continuous feature (attribute) values into discrete bins. This speeds up training and reduces memory usage.

Lightgbm parameters explained

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Welcome to LightGBM’s documentation!¶ LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages: Faster training speed and higher efficiency. Lower memory usage.

Lightgbm parameters explained