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.
5 min tutorial on fitting models with tabular datasets.
In-depth tutorial on controlling various aspects of model fitting.
Using AutoGluon for Kaggle competitions with tabular data.
Modeling data tables with text and numeric/categorical features.
How to predict multiple columns in a data table.
Frequently asked questions about AutoGluon-Tabular.