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Modules


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

 

Supervised


 

Classification

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.

Regression

Predict continuous value by estimating the relationship between dependent and  independent variables.

Common use-cases: Predict Sales, Predict Stock Prices, Predict Quantity, Predict Cost.

Unsupervised


 

Clustering

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

Association Rule Mining

Rule-based machine learning method for discovering interesting relations between variables in database.

Common use-cases: Market Basket Analysis.

Anomaly Detection

Identify and predict rare itemsevents or observations which raise suspicions by differing significantly from the majority of the data.

Common use-cases: Detect bank fraud, structural defect.

Natural Language Processing

Analyze textual data by creating topic models that finds hidden semantic structure in text documents.

Common use-cases: Document Classification, Document Tagging.