Exploratory Data Analysis Tools

This section contains a high-level overview and showcases for exploratory data analysis tools.

Automated Dataset Overview

Get a high-level understanding of datasets including basic statistical information and feature information.

Automated Target Variable Analysis

Analyze and summarize the variable we are trying to predict and it’s relationship with other variables.

Quick Model Fit

Fit a quick model to understand the relationships between the label and the other features in a dataset.

Covariate Shift Detection

Identify situations where the distribution of features in a dataset changes between the training and testing phases, which can lead to biased model performance.

Feature Interaction Charts

Visualize 1/2/3-way relationships between features via chart. The tool automatically picks a chart type given the types of input features.

Anomaly Detection

Explore anomaly detection tools to identify unusual patterns in data and make informed decisions.

Main API Reference

The section contains a reference of base constructs and composite components.

Auto: High-level Composite Components

Reference for high-level composite components.

Base APIs

Components building blocks APIs.

Low-level components API reference

The section contains a reference for low-level components.

autogluon.eda.dataset

Dataset-level APIs

autogluon.eda.interaction

Feature-level interactions APIs

autogluon.eda.missing

Missing data APIs

autogluon.eda.model

Model level APIs

autogluon.eda.shift

Distribution shift APIs

autogluon.eda.transform

Transformations APIs