############ CatBoost ############ `CatBoost `__ is a popular and high-performance 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. CatBoost introduces two critical algorithmic advances to GBDT: * The implementation of ordered boosting, a permutation-driven alternative to the classic algorithm * An innovative algorithm for processing categorical features Both techniques were created to fight a prediction shift caused by a special kind of target leakage present in all currently existing implementations of gradient boosting algorithms. The following table outlines a variety of sample notebooks that address different use cases of Amazon SageMaker CatBoost algorithm. .. list-table:: :widths: 25 25 :header-rows: 1 * - Notebook Title - Description * - `Tabular classification with Amazon SageMaker LightGBM and CatBoost algorithm `__ - This notebook demonstrates the use of the Amazon SageMaker CatBoost 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 CatBoost 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 CatBoost Algorithm `__.