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.

This is a conceptual overview of how LightGBM works. We assume familiarity with decision tree boosting algorithms to focus instead on aspects of LightGBM that may differ from other boosting packages. For detailed algorithms, please refer to the citations or source code. Optimization in Speed and Memory Usage

This document gives a basic walk-through of LightGBM Python-package. List of other helpful links. Python Examples. Python API. Parameters Tuning. Install The preferred way to install LightGBM is via pip:

Dataset (data[, label, reference, weight, ...]). Dataset in LightGBM. Booster ([params, train_set, model_file, ...]). Booster in LightGBM. CVBooster ([model_file ...

This is a quick start guide for LightGBM CLI version. Follow the Installation Guide to install LightGBM first. List of other helpful links. Parameters. Parameters Tuning. Python-package Quick Start. Python API. Training Data Format LightGBM supports input data files with CSV, TSV and LibSVM (zero-based) formats. Files could be both with and ...

This page contains descriptions of all parameters in LightGBM. List of other helpful links. Python API. Parameters Tuning. Parameters Format Parameters are merged together in the following order (later items overwrite earlier ones): LightGBM’s default values. special files for weight, init_score, query, and positions (see Others)

Check http://lightgbm.readthedocs.io/en/latest/Parameters.html for more parameters. Warning **kwargs is not supported in sklearn, it may cause unexpected issues.

Go to LightGBM-complete_source_code_zip/windows folder. Open LightGBM.sln file with Visual Studio, choose Release configuration if you need executable file or DLL configuration if you need shared library and click Build-> Build Solution (Ctrl+Shift+B).

This is a quick start guide for LightGBM CLI version. Follow the Installation Guide to install LightGBM first. List of other helpful links. Parameters. Parameters Tuning. Python-package Quick Start. Python API. Training Data Format LightGBM supports input data files with CSV, TSV and LibSVM (zero-based) formats. Files could be both with and ...

LightGBM offers good accuracy with integer-encoded categorical features. LightGBM applies Fisher (1958) to find the optimal split over categories as described here . This often performs better than one-hot encoding.