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LightGBM
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`LightGBM <https://lightgbm.readthedocs.io/en/latest/>`__ 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.
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* - Notebook Title
- Description
* - `Tabular classification with Amazon SageMaker LightGBM and CatBoost algorithm <https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/lightgbm_catboost_tabular/Amazon_Tabular_Classification_LightGBM_CatBoost.ipynb>`__
- 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 <https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/lightgbm_catboost_tabular/Amazon_Tabular_Regression_LightGBM_CatBoost.ipynb>`__
- 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 <https://docs.aws.amazon.com/sagemaker/latest/dg/nbi.html>`__. 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 <https://docs.aws.amazon.com/sagemaker/latest/dg/lightgbm.html>`__.
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