MultinomialNB

public class MultinomialNB

Multinomial naive bayes classifier.

Multinomial naive bayes classifier used to classify discrete features.

Reference: “Multinomial Naive bayes”

  • Additive smoothing parameter.

    Declaration

    Swift

    public var alpha: Float

  • The prior log probability for each class.

    Declaration

    Swift

    public var classLogPrior: Tensor<Float>

  • Log probability of each word.

    Declaration

    Swift

    public var featureLogProb: Tensor<Float>

  • Unique classes in target value set.

    Declaration

    Swift

    public var classes: Tensor<Int32>

  • Tensor contains the index of class in classes.

    Declaration

    Swift

    public var indices: Tensor<Int32>

  • Create a multinomial naive bayes model.

    Declaration

    Swift

    public init(
    alpha:Float = 1.0
)

    Parameters

    alpha

    Additive smoothing parameter, default to 1.0.

  • Fit a multinomial naive bayes classifier model.

    Declaration

    Swift

    public func fit(data: Tensor<Float>, labels: Tensor<Int32>)

    Parameters

    data

    Training data with shape [sample count, feature count].
    labels

    Target values with shape [sample count].

  • Returns log-probability estimates for the test data.

    Declaration

    Swift

    public func predictLogProba(data: Tensor<Float>) -> Tensor<Float>

    Parameters

    data

    Test data with shape [sample count, feature count].

    Return Value

    log-probability estimates for the test data.

  • Returns classified input data.

    Declaration

    Swift

    public func prediction(for data: Tensor<Float>) -> Tensor<Int32>

    Parameters

    data

    Input data with shape [sample count, feature count].

    Return Value

    Classification of input data.

  • Returns mean accuracy on the given test data and labels.

    Declaration

    Swift

    public func score(data: Tensor<Float>, labels: Tensor<Int32>) -> Float

    Parameters

    data

    Sample data with shape [sample count, feature count].
    labels

    Target values with shape [sample count].

    Return Value

    Returns the mean accuracy on the given test data and labels.