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Machine Learning
A probabilistic model that represents normally distributed subpopulations within an overall population using a mixture of Gaussian distributions.
Detailed Explanation
Gaussian Mixture Models (GMMs) are probabilistic models used in machine learning to describe a dataset as a combination of multiple Gaussian (normal) distributions. Each component captures a subpopulation within the data, enabling tasks like clustering and density estimation. GMMs assume that data points are generated from these overlapping distributions, providing a flexible way to model complex, multimodal data structures.
Use Cases
- •Cluster customer segmentation data to identify distinct groups within a market for targeted marketing strategies.
Related Terms
Other terms in the Machine Learning category