Why is GMM useful?

GMM is useful because it provides a flexible framework for modeling complex data distributions. By assuming that the data is generated from a mixture of multiple Gaussian distributions, GMM can capture more nuanced patterns and structures in the data, leading to improved performance in various machine learning tasks such as clustering and density estimation.