"KernelDensityEstimation" (Machine Learning Method)

Details & Suboptions

  • "KernelDensityEstimation" is a nonparametric method that models the probability density of a numeric space with a mixture of simple distributions (called kernels) centered around each training example, as in KernelMixtureDistribution.
  • The probability density function for a vector is given by for a kernel function , kernel size and a number of training examples m.
  • The following options can be given:
  • Method "Fixed"kernel size method
    "KernelSize" Automaticsize of the kernels when Method"Fixed"
    "KernelType" "Gaussian"type of kernel used
    "NeighborsNumber" Automatickernel size expressed as a number of neighbors
  • Possible settings for "KernelType" include:
  • "Gaussian"each kernel is a Gaussian distribution
    "Ball"each kernel is a uniform distribution on a ball
  • Possible settings for Method include:
  • "Adaptive"kernel sizes can differ from each other
    "Fixed"all kernels have the same size
  • When "KernelType""Gaussian", each kernel is a spherical Gaussian (product of independent normal distributions ), and "KernelSize" h refers to the standard deviation of the normal distribution.
  • When "KernelType""Ball", each kernel is a uniform distribution inside a sphere, and "KernelSize" refers to the radius of the sphere.
  • The value of "NeighborsNumber"k is converted into kernel size(s), so that a kernel centered around a training example typically "contains" k other training examples. If "KernelType""Ball", "contains" refers to examples that are inside the ball. If "KernelType""Gaussian", "contains" refers to examples that are inside a ball of radius h where n is the dimension of the data.
  • When Method"Fixed" and "NeighborsNumber"k, a unique kernel size is found such that training examples contain on average k other examples.
  • When Method"Adaptive" and "NeighborsNumber"k, each training example adapts its kernel size such that it contains about k other examples.
  • Because of preprocessing, the "NeighborsNumber" option is typically a more convenient way to control kernel sizes than "KernelSize". When Method"Fixed", the value of "KernelSize" supersedes the value of "NeighborsNumber".
  • Information[LearnedDistribution[…],"MethodOption"] can be used to extract the values of options chosen by the automation system.
  • LearnDistribution[…,FeatureExtractor"Minimal"] can be used to remove most preprocessing and directly access the method.

Examples

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Basic Examples  (3)

Train a "KernelDensityEstimation" distribution on a numeric dataset:

Look at the distribution Information:

Obtain options information:

Obtain an option value directly:

Compute the probability density for a new example:

Plot the PDF along with the training data:

Generate and visualize new samples:

Train a "KernelDensityEstimation" distribution on a two-dimensional dataset:

Plot the PDF along with the training data:

Use SynthesizeMissingValues to impute missing values using the learned distribution:

Train a "KernelDensityEstimation" distribution on a nominal dataset:

Because of the necessary preprocessing, the PDF computation is not exact:

Use ComputeUncertainty to obtain the uncertainty on the result:

Increase MaxIterations to improve the estimation precision:

Options  (4)

"KernelSize"  (1)

Train a kernel mixture distribution with a kernel size of 0.2:

Evaluate the PDF of the distribution at a specific point:

Visualize the PDF obtained after training a kernel mixture distribution with various kernel sizes:

"KernelType"  (1)

Train a "KernelDensityEstimation" distribution with a "Ball" kernel:

Evaluate the PDF of the distribution at a specific point:

Visualize the PDF obtained after training a kernel mixture distribution with a "Ball" and a "Gaussian" kernel:

Method  (1)

Train a "KernelDensityEstimation" distribution with the "Adaptive" method:

Evaluate the PDF of the distribution at a specific point:

Visualize the PDF obtained after training a kernel mixture distribution with a "Ball" and a "Gaussian" kernel:

"NeighborsNumber"  (1)

Train a kernel mixture distribution with a kernel size of about 10 neighbors:

Evaluate the PDF of the distribution at a specific point:

Visualize the PDF obtained after training a kernel mixture distribution with various kernel sizes expressed as neighbors numbers: