LightGBMΒΆ

LightGBM is a popular and efficient open-source implementation of the Gradient Boosting Decision Tree (GBDT) algorithm. GBDT is a supervised learning algorithm that attempts to accurately predict a target variable by combining an ensemble of estimates from a set of simpler and weaker models. LightGBM uses additional techniques to significantly improve the efficiency and scalability of conventional GBDT.

The following table outlines a variety of sample notebooks that address different use cases of Amazon SageMaker LightGBM algorithm.

Notebook Title

Description

Tabular classification with Amazon SageMaker LightGBM and CatBoost algorithm

This notebook demonstrates the use of the Amazon SageMaker LightGBM algorithm to train and host a tabular classification model.

Tabular regression with Amazon SageMaker LightGBM and CatBoost algorithm

This notebook demonstrates the use of the Amazon SageMaker LightGBM algorithm to train and host a tabular regression model.

For instructions on how to create and access Jupyter notebook instances that you can use to run the example in SageMaker, see Use Amazon SageMaker Notebook Instances. After you have created a notebook instance and opened it, choose the SageMaker Examples tab to see a list of all of the SageMaker samples. To open a notebook, choose its Use tab and choose Create copy.

For detailed documentation, please refer to the Sagemaker LightGBM Algorithm.