How deeply does Machine Learning Algorithms in Depth cover density estimator?

Explains KDE centers, smoothing effects, and tree-based query acceleration without extending beyond these implementation concerns.

Density estimator

As explained in Machine Learning Algorithms in Depth

A density estimate is an estimated probability density function inferred from observed data or a statistical model.

A density estimator models the probability density of observed data. In unsupervised learning, it provides a way to represent how likely different regions of the data space are. section 9.2 section 9.8

Kernel density estimation supplies the central computational treatment:

Allocate one kernel center to each data point, then combine the kernels into an estimated density. section 9.8
Adjust the smoothing parameter to control the resulting estimate and observe how smoothing changes its shape. section 9.6
Reduce query-time complexity with ball trees or KD trees when evaluating the estimate more efficiently. section 9.6

Computational-biology density modeling

Apply kernel density estimation to computational-biology data, compare estimates produced with different smoothing parameters, and optimize later queries with a ball tree or KD tree. section 9.2 section 9.6

How Machine Learning Algorithms in Depth covers it

Cover of Machine Learning Algorithms in Depth

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Related concepts

unsupervised learningkind of · Unsupervised learning is a machine-learning approach that discovers patterns, structure, or representations in data without labeled target outputs.
kernel density estimateincludes · A kernel density estimate is a nonparametric probability-density estimate formed by combining localized kernel functions centered on observed data points.
anomaly detectionuses · Anomaly detection is the identification of observations, events, or behaviors that substantially deviate from expected patterns or a normal baseline.
probability densityproduces · A probability density function is a nonnegative function describing the distribution of a continuous random variable, whose integral over a region gives the probability of values in that region.

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