This page provides an overview of all the Modules available in PyCaret. A Module is a building block for creating experiments. Each module encapsulates specific machine learning algorithms and functions that are consistently used across different modules. For example, the create_model function trains and evaluates a model in all modules. Depending on the type of experiment, one of the six available modules must be imported into the environment. Learn more about how to import a module in notebook.
Classify elements into groups and predict class labels which are discrete and unordered.
Common use-cases: Spam Detection, Predict Customer Churn, Predict Default, Disease Found / Not Found.
Automatic grouping of similar objects into sets in a way that objects in same groups are more similar to each other than to those in other groups.
Common use-cases: Customer segmentation, pattern analysis
Identify and predict rare items, events or observations which raise suspicions by differing significantly from the majority of the data.
Common use-cases: Detect bank fraud, structural defect.