Tabular Prediction

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 Using FITtabular-quickstart.html

5 min tutorial on fitting models with tabular datasets.

In-depth FIT Tutorialtabular-indepth.html

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

Deployment Optimizationtabular-deployment.html

Tutorial on optimizing the predictor artifact for production deployment.

Kaggle Tutorialtabular-kaggle.html

Using AutoGluon for Kaggle competitions with tabular data.

Data Tables Containing Image, Text, and Tabulartabular-multimodal.html

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

Data Tables Containing Texttabular-multimodal-text-others.html

Modeling data tables with text and numeric/categorical features.

Interpretable rule-based modelingtabular-interpretability.html

Fitting interpretable models to data table for understanding data and predictions.

Training models with GPU supporttabular-gpu.html

How to train models with GPU support.

Multi-Label Predictiontabular-multilabel.html

How to predict multiple columns in a data table.

Adding a Custom Modeltabular-custom-model.html

How to add a custom model to AutoGluon.

Adding a Custom Model (Advanced)tabular-custom-model-advanced.html

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

Adding a Custom Metrictabular-custom-metric.html

How to add a custom metric to AutoGluon.

Feature Engineeringtabular-feature-engineering.html

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

FAQtabular-faq.html

Frequently asked questions about AutoGluon-Tabular.