BernoulliNB

public class BernoulliNB

Bernoulli naive bayes classifier.

Bernoulli naive bayes classifier used to classify discrete binary features.

Reference: “Bernoulli 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 bernoulli naive model.

    Declaration

    Swift

    public init(
    alpha: Float = 1.0
)

    Parameters

    alpha

    Additive smoothing parameter, default to 1.0.

  • Fit a bernoulli 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 value with shape [sample count].

  • Returns log-probability estimates for the input data.

    Declaration

    Swift

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

    Parameters

    data

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

    Return Value

    log-probability estimates for the input data.

  • Returns classification of 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 input 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 label with shape [sample count].

    Return Value

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