Built-in SageMaker Algorithms for Tabular Data - Amazon SageMaker

Built-in SageMaker Algorithms for Tabular Data

Amazon SageMaker provides built-in algorithms that are tailored to the analysis of tabular data. Tabular data refers to any datasets that are organized in tables consisting of rows (observations) and columns (features). The built-in SageMaker algorithms for tabular data can be used for either classification or regression problems.

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

Algorithm name Channel name Training input mode File type Instance class Parallelizable
AutoGluon-Tabular training and (optionally) validation File CSV CPU or GPU (single instance only) No
CatBoost training and (optionally) validation File CSV CPU (single instance only) No
Factorization Machines train and (optionally) test File or Pipe recordIO-protobuf CPU (GPU for dense data) Yes
K-Nearest-Neighbors (k-NN) train and (optionally) test File or Pipe recordIO-protobuf or CSV CPU or GPU (single GPU device on one or more instances) Yes
LightGBM training and (optionally) validation File CSV CPU (single instance only) No
Linear Learner train and (optionally) validation, test, or both File or Pipe recordIO-protobuf or CSV CPU or GPU Yes
TabTransformer training and (optionally) validation File CSV CPU or GPU (single instance only) No
XGBoost (0.90-1, 0.90-2, 1.0-1, 1.2-1, 1.2-21) train and (optionally) validation File or Pipe CSV, LibSVM, or Parquet CPU (or GPU for 1.2-1) Yes