Density Estimation: Concept map

Density estimation walks the line between unsupervised learning, feature engineering, and data modeling. Some of the most popular and useful density estimation techniques are mixture models such as Gaussian Mixtures (sklearn.mixture.GaussianMixture), and neighbor-based approaches such as the kernel density estimate (sklearn.neighbors.KernelDensity). Gaussian Mixtures are discussed more fully in the context of clustering, because the technique is also useful as an unsupervised clustering scheme.

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