# Built-in SageMaker AI Algorithms for Tabular Data
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Amazon SageMaker AI 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 AI algorithms for tabular data can be used for either classification or regression problems.
+ [AutoGluon-Tabular](autogluon-tabular.md)—an open-source AutoML framework that succeeds by ensembling models and stacking them in multiple layers. 
+ [CatBoost](catboost.md)—an implementation of the gradient-boosted trees algorithm that introduces ordered boosting and an innovative algorithm for processing categorical features.
+ [Factorization Machines Algorithm](fact-machines.md)—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](k-nearest-neighbors.md)—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](lightgbm.md)—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](linear-learner.md)—learns a linear function for regression or a linear threshold function for classification.
+ [TabTransformer](tabtransformer.md)—a novel deep tabular data modeling architecture built on self-attention-based Transformers. 
+ [XGBoost algorithm with Amazon SageMaker AI](xgboost.md)—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 |