# CatBoost
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[CatBoost](https://catboost.ai/) 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:

1. The implementation of ordered boosting, a permutation-driven alternative to the classic algorithm

1. 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. This page includes information about Amazon EC2 instance recommendations and sample notebooks for CatBoost.

## Amazon EC2 instance recommendation for the CatBoost algorithm
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SageMaker AI CatBoost currently only trains using CPUs. CatBoost is a memory-bound (as opposed to compute-bound) algorithm. So, a general-purpose compute instance (for example, M5) is a better choice than a compute-optimized instance (for example, C5). Further, we recommend that you have enough total memory in selected instances to hold the training data. 

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


****  

| **Notebook Title** | **Description** | 
| --- | --- | 
|  [Tabular classification with Amazon SageMaker AI 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 AI CatBoost algorithm to train and host a tabular classification model.   | 
|  [Tabular regression with Amazon SageMaker AI 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 AI 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 AI, see [Amazon SageMaker notebook instances](nbi.md). After you have created a notebook instance and opened it, choose the **SageMaker AI Examples** tab to see a list of all of the SageMaker AI samples. To open a notebook, choose its **Use** tab and choose **Create copy**.