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CatBoost
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`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 `__.