Back to GalleryGaussian Mixture Model
Gaussian Mixture Models are probabilistic models that assume all data points are generated from a mixture of a finite number of Gaussian distributions with unknown parameters. GMMs perform 'soft clustering', calculating a probability distribution over all clusters for each point.
Key Parameters & Visual Influence:
• Components (K): The number of Gaussian distributions to fit.
• Covariance Type: 'Spherical' restricts the Gaussian contours to circles. 'Full' allows ellipses to stretch, rotate, and scale in any direction, capturing correlated variables.
Dataset Studio
CSV format: Headers in first row, last column = label/target. All values must be numeric.
Example: x1, x2, label