Module: SVMKit::Base::Classifier

Included in:
Ensemble::AdaBoostClassifier, Ensemble::RandomForestClassifier, KernelMachine::KernelSVC, LinearModel::LogisticRegression, LinearModel::SVC, Multiclass::OneVsRestClassifier, NaiveBayes::BaseNaiveBayes, NearestNeighbors::KNeighborsClassifier, PolynomialModel::FactorizationMachineClassifier, Tree::DecisionTreeClassifier
Defined in:
lib/svmkit/base/classifier.rb

Overview

Module for all classifiers in SVMKit.

Instance Method Summary collapse

Instance Method Details

#fit ⇒ Object

An abstract method for fitting a model.

Raises:

  • (NotImplementedError) 


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# File 'lib/svmkit/base/classifier.rb', line 11

def fit
  raise NotImplementedError, "#{__method__} has to be implemented in #{self.class}."
end

#predict ⇒ Object

An abstract method for predicting labels.

Raises:

  • (NotImplementedError) 


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# File 'lib/svmkit/base/classifier.rb', line 16

def predict
  raise NotImplementedError, "#{__method__} has to be implemented in #{self.class}."
end

#score(x, y) ⇒ Float

Calculate the mean accuracy of the given testing data.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_samples, n_features]) Testing data.

  • y (Numo::Int32) —

    (shape: [n_samples]) True labels for testing data.

Returns:

  • (Float) —

    Mean accuracy



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# File 'lib/svmkit/base/classifier.rb', line 25

def score(x, y)
  SVMKit::Validation.check_sample_array(x)
  SVMKit::Validation.check_label_array(y)
  SVMKit::Validation.check_sample_label_size(x, y)
  evaluator = SVMKit::EvaluationMeasure::Accuracy.new
  evaluator.score(y, predict(x))
end