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).

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.

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.

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.

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 uses a custom approach for finding optimal splits for categorical features. In this process, LightGBM explores splits that break a categorical feature into two groups.

Python API Data Structure APITraining API