Package software.amazon.awssdk.services.comprehend.model

Interface ClassifierEvaluationMetrics.Builder

    • Method Detail

      • accuracy

        ClassifierEvaluationMetrics.Builder accuracy​(Double accuracy)

        The fraction of the labels that were correct recognized. It is computed by dividing the number of labels in the test documents that were correctly recognized by the total number of labels in the test documents.

        Parameters:
        accuracy - The fraction of the labels that were correct recognized. It is computed by dividing the number of labels in the test documents that were correctly recognized by the total number of labels in the test documents.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • precision

        ClassifierEvaluationMetrics.Builder precision​(Double precision)

        A measure of the usefulness of the classifier results in the test data. High precision means that the classifier returned substantially more relevant results than irrelevant ones.

        Parameters:
        precision - A measure of the usefulness of the classifier results in the test data. High precision means that the classifier returned substantially more relevant results than irrelevant ones.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • recall

        ClassifierEvaluationMetrics.Builder recall​(Double recall)

        A measure of how complete the classifier results are for the test data. High recall means that the classifier returned most of the relevant results.

        Parameters:
        recall - A measure of how complete the classifier results are for the test data. High recall means that the classifier returned most of the relevant results.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • f1Score

        ClassifierEvaluationMetrics.Builder f1Score​(Double f1Score)

        A measure of how accurate the classifier results are for the test data. It is derived from the Precision and Recall values. The F1Score is the harmonic average of the two scores. The highest score is 1, and the worst score is 0.

        Parameters:
        f1Score - A measure of how accurate the classifier results are for the test data. It is derived from the Precision and Recall values. The F1Score is the harmonic average of the two scores. The highest score is 1, and the worst score is 0.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • microPrecision

        ClassifierEvaluationMetrics.Builder microPrecision​(Double microPrecision)

        A measure of the usefulness of the recognizer results in the test data. High precision means that the recognizer returned substantially more relevant results than irrelevant ones. Unlike the Precision metric which comes from averaging the precision of all available labels, this is based on the overall score of all precision scores added together.

        Parameters:
        microPrecision - A measure of the usefulness of the recognizer results in the test data. High precision means that the recognizer returned substantially more relevant results than irrelevant ones. Unlike the Precision metric which comes from averaging the precision of all available labels, this is based on the overall score of all precision scores added together.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • microRecall

        ClassifierEvaluationMetrics.Builder microRecall​(Double microRecall)

        A measure of how complete the classifier results are for the test data. High recall means that the classifier returned most of the relevant results. Specifically, this indicates how many of the correct categories in the text that the model can predict. It is a percentage of correct categories in the text that can found. Instead of averaging the recall scores of all labels (as with Recall), micro Recall is based on the overall score of all recall scores added together.

        Parameters:
        microRecall - A measure of how complete the classifier results are for the test data. High recall means that the classifier returned most of the relevant results. Specifically, this indicates how many of the correct categories in the text that the model can predict. It is a percentage of correct categories in the text that can found. Instead of averaging the recall scores of all labels (as with Recall), micro Recall is based on the overall score of all recall scores added together.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • microF1Score

        ClassifierEvaluationMetrics.Builder microF1Score​(Double microF1Score)

        A measure of how accurate the classifier results are for the test data. It is a combination of the Micro Precision and Micro Recall values. The Micro F1Score is the harmonic mean of the two scores. The highest score is 1, and the worst score is 0.

        Parameters:
        microF1Score - A measure of how accurate the classifier results are for the test data. It is a combination of the Micro Precision and Micro Recall values. The Micro F1Score is the harmonic mean of the two scores. The highest score is 1, and the worst score is 0.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • hammingLoss

        ClassifierEvaluationMetrics.Builder hammingLoss​(Double hammingLoss)

        Indicates the fraction of labels that are incorrectly predicted. Also seen as the fraction of wrong labels compared to the total number of labels. Scores closer to zero are better.

        Parameters:
        hammingLoss - Indicates the fraction of labels that are incorrectly predicted. Also seen as the fraction of wrong labels compared to the total number of labels. Scores closer to zero are better.
        Returns:
        Returns a reference to this object so that method calls can be chained together.