How AutoGluon-Tabular works

AutoGluon-Tabular performs advanced data processing, deep learning, and multi-layer model ensemble methods. It automatically recognizes the data type in each column for robust data preprocessing, including special handling of text fields.

AutoGluon fits various models ranging from off-the-shelf boosted trees to customized neural networks. These models are ensembled in a novel way: models are stacked in multiple layers and trained in a layer-wise manner that guarantees raw data can be translated into high-quality predictions within a given time constraint. This process mitigates overfitting by splitting the data in various ways with careful tracking of out-of-fold examples.

The AutoGluon-Tabular algorithm performs well in machine learning competitions because of its robust handling of a variety of data types, relationships, and distributions. You can use AutoGluon-Tabular for regression, classification (binary and multiclass), and ranking problems.

Refer to the following diagram illustrating how the multi-layer stacking strategy works.

For more information, see AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data.