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Machine Learning with Spark - Second Edition by Nick Pentreath, Manpreet Singh Ghotra, Rajdeep Dua

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Gaussian Mixture Model

A mixture model is a probabilistic model of a sub-population within a population. These models are used to make statistical inferences about a sub-population, given the observations of pooled populations.

A Gaussian Mixture Model (GMM) is a mixture model represented as a weighted sum of Gaussian component densities. Its model coefficients are estimated from training data using the iterative Expectation-Maximization (EM) algorithm or Maximum A Posteriori (MAP) estimation from a trained model.

The spark.ml implementation uses the EM algorithm.

It has the following parameters:

  • k: Number of desired clusters
  • convergenceTol: Maximum change in log-likelihood at which one considers convergence achieved
  • maxIterations: Maximum ...

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