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Online Processing

You can use the Naïve Bayes classifier algorithm in the online processing mode only at the training stage.

This computation mode assumes that the data arrives in blocks \(i = 1, 2, 3, \ldots, \text{nblocks}\).

Training

Naïve Bayes classifier training in the online processing mode follows the general workflow described in Classification Usage Model.

Naïve Bayes classifier in the online processing mode accepts the input described below. Pass the Input ID as a parameter to the methods that provide input for your algorithm. For more details, see Algorithms.

Training Input for Naïve Bayes Classifier (Online Processing)

Input ID

Input

data

Pointer to the \(n_i \times p\) numeric table that represents the current data block.

labels

Pointer to the \(n_i \times 1\) numeric table with class labels associated with the current data block.

Note

These tables can be objects of any class derived from NumericTable.

Naïve Bayes classifier in the online processing mode has the following parameters:

Training Parameters for Naïve Bayes Classifier (Online Processing)

Parameter

Default Value

Description

algorithmFPType

float

The floating-point type that the algorithm uses for intermediate computations. Can be float or double.

method

defaultDense

Available computation methods for the Naïve Bayes classifier:

  • defaultDense - default performance-oriented method

  • fastCSR - performance-oriented method for CSR numeric tables

nClasses

Not applicable

The number of classes. A required parameter.

priorClassEstimates

\(1/\text{nClasses}\)

Vector of size nClasses that contains prior class estimates. The default value applies to each vector element.

alpha

\(1\)

Vector of size \(p\) that contains the imagined occurrences of features. The default value applies to each vector element.

For a description of the output, refer to Classification Usage Model.

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Distributed Processing