Tabular

For standard datasets that are represented as tables (stored as CSV file, parquet from database, etc.), AutoGluon can produce models to predict the values in one column based on the values in the other columns. With just a single call to fit(), you can achieve high accuracy in standard supervised learning tasks (both classification and regression), without dealing with cumbersome issues like data cleaning, feature engineering, hyperparameter optimization, model selection, etc.

Quick Start

5 min tutorial on fitting models with tabular datasets.

Essentials

Essential information about the most important settings for tabular prediction.

In-depth

In-depth tutorial on controlling various aspects of model fitting.

Data Tables Containing Image, Text, and Tabular

Modeling data tables with image, text, numeric, and categorical features.

Feature Engineering

AutoGluon’s default feature engineering and how to extend it.

Multi-Label Prediction

How to predict multiple columns in a data table.

Kaggle Tutorial

Using AutoGluon for Kaggle competitions with tabular data.

Training models with GPU support

How to train models with GPU support.

Adding a Custom Metric

How to add a custom metric to AutoGluon.

Adding a Custom Model

How to add a custom model to AutoGluon.

Adding a Custom Model (Advanced)

How to add a custom model to AutoGluon (Advanced).

Deployment Optimization

Tutorial on optimizing the predictor artifact for production deployment.