Package com.google.cloud.automl.v1

Interface ClassificationEvaluationMetrics.ConfidenceMetricsEntryOrBuilder

All Superinterfaces:
com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder
All Known Implementing Classes:
ClassificationEvaluationMetrics.ConfidenceMetricsEntry, ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder
Enclosing class:
ClassificationEvaluationMetrics
public static interface ClassificationEvaluationMetrics.ConfidenceMetricsEntryOrBuilder extends com.google.protobuf.MessageOrBuilder

  • Method Details

    • getConfidenceThreshold

      float getConfidenceThreshold()
       Output only. Metrics are computed with an assumption that the model
 never returns predictions with score lower than this value.
      float confidence_threshold = 1;
      Returns:
      The confidenceThreshold.

    • getPositionThreshold

      int getPositionThreshold()
       Output only. Metrics are computed with an assumption that the model
 always returns at most this many predictions (ordered by their score,
 descendingly), but they all still need to meet the confidence_threshold.
      int32 position_threshold = 14;
      Returns:
      The positionThreshold.

    • getRecall

      float getRecall()
       Output only. Recall (True Positive Rate) for the given confidence
 threshold.
      float recall = 2;
      Returns:
      The recall.

    • getPrecision

      float getPrecision()
       Output only. Precision for the given confidence threshold.
      float precision = 3;
      Returns:
      The precision.

    • getFalsePositiveRate

      float getFalsePositiveRate()
       Output only. False Positive Rate for the given confidence threshold.
      float false_positive_rate = 8;
      Returns:
      The falsePositiveRate.

    • getF1Score

      float getF1Score()
       Output only. The harmonic mean of recall and precision.
      float f1_score = 4;
      Returns:
      The f1Score.

    • getRecallAt1

      float getRecallAt1()
       Output only. The Recall (True Positive Rate) when only considering the
 label that has the highest prediction score and not below the confidence
 threshold for each example.
      float recall_at1 = 5;
      Returns:
      The recallAt1.

    • getPrecisionAt1

      float getPrecisionAt1()
       Output only. The precision when only considering the label that has the
 highest prediction score and not below the confidence threshold for each
 example.
      float precision_at1 = 6;
      Returns:
      The precisionAt1.

    • getFalsePositiveRateAt1

      float getFalsePositiveRateAt1()
       Output only. The False Positive Rate when only considering the label that
 has the highest prediction score and not below the confidence threshold
 for each example.
      float false_positive_rate_at1 = 9;
      Returns:
      The falsePositiveRateAt1.

    • getF1ScoreAt1

      float getF1ScoreAt1()
       Output only. The harmonic mean of [recall_at1][google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.recall_at1] and [precision_at1][google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.precision_at1].
      float f1_score_at1 = 7;
      Returns:
      The f1ScoreAt1.

    • getTruePositiveCount

      long getTruePositiveCount()
       Output only. The number of model created labels that match a ground truth
 label.
      int64 true_positive_count = 10;
      Returns:
      The truePositiveCount.

    • getFalsePositiveCount

      long getFalsePositiveCount()
       Output only. The number of model created labels that do not match a
 ground truth label.
      int64 false_positive_count = 11;
      Returns:
      The falsePositiveCount.

    • getFalseNegativeCount

      long getFalseNegativeCount()
       Output only. The number of ground truth labels that are not matched
 by a model created label.
      int64 false_negative_count = 12;
      Returns:
      The falseNegativeCount.

    • getTrueNegativeCount

      long getTrueNegativeCount()
       Output only. The number of labels that were not created by the model,
 but if they would, they would not match a ground truth label.
      int64 true_negative_count = 13;
      Returns:
      The trueNegativeCount.