CatBoost
CatBoost introduces two critical algorithmic advances to GBDT:
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The implementation of ordered boosting, a permutation-driven alternative to the classic algorithm
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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
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
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 |
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 |
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. 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.
