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: …

LightGBM can use categorical features as input directly. It doesn’t need to convert to one-hot encoding, and is much faster than one-hot encoding (about 8x speed-up).

More specifically, LightGBM sorts the histogram (for a categorical feature) according to its accumulated values (sum_gradient / sum_hessian) and then finds the best split on the sorted histogram.

The most important parameters which new users should take a look at are located into Core Parameters and the top of Learning Control Parameters sections of the full detailed list of LightGBM’s parameters.

See Callbacks in Python API for more information. init_model (str, pathlib.Path, Booster, LGBMModel or None, optional (default=None)) – Filename of LightGBM model, Booster instance or LGBMModel …

LightGBM will randomly select a subset of features on each iteration (tree) if feature_fraction is smaller than 1.0. For example, if you set it to 0.8, LightGBM will select 80% of features before training each tree

LightGBM offers good accuracy with integer-encoded categorical features. LightGBM applies Fisher (1958) to find the optimal split over categories as described here.

lightgbm is tested automatically on every commit, across many combinations of operating system, R version, and compiler. This section describes how to test the package locally while you are developing.

init_model (str, pathlib.Path, Booster, LGBMModel or None, optional (default=None)) – Filename of LightGBM model, Booster instance or LGBMModel instance used for continue training.

LightGBM comes with several parameters that can be used to control the number of nodes per tree. The suggestions below will speed up training, but might hurt training accuracy.