|  | ## Naive Bayes (Kernel)

#### Synopsis

Returns classification model using estimated kernel densities.

#### Description

Kernel Naive Bayes learner.

#### Input

- training set: expects: ExampleSet

#### Output

- model:
- exampleSet:

#### Parameters

- laplace correction: Use Laplace correction to prevent high influence of zero probabilities.
- estimation mode: The kernel density estimation mode.
- bandwidth selection: The method to set the kernel bandwidth.
- bandwidth: Kernel bandwidth.
- minimum bandwidth: Minimum kernel bandwidth.
- number of kernels: Number of kernels.
- use application grid: Use a kernel density function grid in model application. (Speeds up model application at the expense of the density function precision.)
- application grid size: Size of the application grid.

#### ExampleProcess