Em algorithm in python4/7/2024 Higher the value of sigma, the more it would be spread out. µ is the mean, which is the centre of the distribution and the σ is standard deviation, which is range of the distribution. In single dimensions, the normal distribution has two parameters, µ (mu) and σ (sigma). GMM uses the Expectation-Maximization (EM) Algorithm which is used to find the optimal value for mean, covariance matrix and mixing coefficients. This pattern is the basis for comprehending the working of this model. Normal distribution is a well-known concept where the data points are symmetrically distributed close to the mean value and it is visually represented in the form of a bell curve, which is the probability density function. ![]() It assumes that all classes are distributed in a gaussian distribution, which is the same as normal distribution but is two-dimensional. Gaussian Mixture Model is a probability-based distribution model. Gaussian Mixture Model is a clustering model that is used in unsupervised machine learning to classify and identify both univariate and multivariate classes. Several data points grouped together into various clusters based on their similarity is called clustering. ![]() Implementing Gaussian Mixture Model using Expectation Maximization (EM) Algorithm in Python on IRIS dataset.
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