Built-in SageMaker Algorithms for Tabular Data

AutoGluon-Tabular—an open-source AutoML framework that succeeds by ensembling models and stacking them in multiple layers.

CatBoost—an implementation of the gradient-boosted trees algorithm that introduces ordered boosting and an innovative algorithm for processing categorical features.

Factorization Machines Algorithm—an extension of a linear model that is designed to economically capture interactions between features within high-dimensional sparse datasets.

K-Nearest Neighbors (k-NN) Algorithm—a non-parametric method that uses the k nearest labeled points to assign a label to a new data point for classification or a predicted target value from the average of the k nearest points for regression.

LightGBM—an implementation of the gradient-boosted trees algorithm that adds two novel techniques for improved efficiency and scalability: Gradient-based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB).

Linear Learner Algorithm—learns a linear function for regression or a linear threshold function for classification.

TabTransformer—a novel deep tabular data modeling architecture built on self-attention-based Transformers.

XGBoost algorithm with Amazon SageMaker—an implementation of the gradient-boosted trees algorithm that combines an ensemble of estimates from a set of simpler and weaker models.