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).

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.

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.

Using the lightgbm () function In a first step, you need to convert data to numeric. Afterwards, you are ready to fit the model by the lightgbm () function.

Python API Data Structure APITraining API

You will see two binaries are generated, lightgbm and lib_lightgbm.so. If you are building on macOS, you probably need to remove macro BOOST_COMPUTE_USE_OFFLINE_CACHE in …

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.

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