What's New with AWS: Amazon SageMaker built-in algorithms now provides four new Tabular Data Modeling Algorithms

• amazon-sagemaker-examples

  17 9,491 9.3 Jupyter Notebook

  Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

  

Amazon SageMaker provides four new tabular data modeling algorithms: LightGBM, CatBoost, AutoGluon-Tabular and TabTransformer. These popular, state-of-the-art algorithms can be used for both tabular classification and regression tasks. They are available through the SageMaker JumpStart UI inside of SageMaker Studio, as well as through python code using SageMaker Python SDK. To learn how to use these algorithms, you can find SageMaker example notebooks below:

• LightGBM

  11 16,043 9.2 C++

  A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

  

LightGBM is a popular and high-performance open-source implementation of the Gradient Boosting Decision Tree (GBDT). To learn how to use this algorithm, please see example notebooks for Classification and Regression.

• WorkOS

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  The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

  

• catboost

  8 7,744 9.9 Python

  A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

  

CatBoost is another popular and high-performance open-source implementation of the Gradient Boosting Decision Tree (GBDT). To learn how to use this algorithm, please see example notebooks for Classification and Regression.

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