Uses of Class
com.google.api.services.language.v2.model.XPSConfidenceMetricsEntry

Packages that use XPSConfidenceMetricsEntry

Package	Description
com.google.api.services.language.v2.model

Uses of XPSConfidenceMetricsEntry in com.google.api.services.language.v2.model

Methods in com.google.api.services.language.v2.model that return XPSConfidenceMetricsEntry

Modifier and Type	Method and Description
XPSConfidenceMetricsEntry	XPSConfidenceMetricsEntry.clone()
XPSConfidenceMetricsEntry	XPSTextExtractionEvaluationMetrics.getBestF1ConfidenceMetrics() Values are at the highest F1 score on the precision-recall curve.
XPSConfidenceMetricsEntry	XPSConfidenceMetricsEntry.set(String fieldName, Object value)
XPSConfidenceMetricsEntry	XPSConfidenceMetricsEntry.setConfidenceThreshold(Float confidenceThreshold) Metrics are computed with an assumption that the model never return predictions with score lower than this value.
XPSConfidenceMetricsEntry	XPSConfidenceMetricsEntry.setF1Score(Float f1Score) The harmonic mean of recall and precision.
XPSConfidenceMetricsEntry	XPSConfidenceMetricsEntry.setF1ScoreAt1(Float f1ScoreAt1) The harmonic mean of recall_at1 and precision_at1.
XPSConfidenceMetricsEntry	XPSConfidenceMetricsEntry.setFalseNegativeCount(Long falseNegativeCount) The number of ground truth labels that are not matched by a model created label.
XPSConfidenceMetricsEntry	XPSConfidenceMetricsEntry.setFalsePositiveCount(Long falsePositiveCount) The number of model created labels that do not match a ground truth label.
XPSConfidenceMetricsEntry	XPSConfidenceMetricsEntry.setFalsePositiveRate(Float falsePositiveRate) False Positive Rate for the given confidence threshold.
XPSConfidenceMetricsEntry	XPSConfidenceMetricsEntry.setFalsePositiveRateAt1(Float falsePositiveRateAt1) The False Positive Rate when only considering the label that has the highest prediction score and not below the confidence threshold for each example.
XPSConfidenceMetricsEntry	XPSConfidenceMetricsEntry.setPositionThreshold(Integer positionThreshold) 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.
XPSConfidenceMetricsEntry	XPSConfidenceMetricsEntry.setPrecision(Float precision) Precision for the given confidence threshold.
XPSConfidenceMetricsEntry	XPSConfidenceMetricsEntry.setPrecisionAt1(Float precisionAt1) The precision when only considering the label that has the highest prediction score and not below the confidence threshold for each example.
XPSConfidenceMetricsEntry	XPSConfidenceMetricsEntry.setRecall(Float recall) Recall (true positive rate) for the given confidence threshold.
XPSConfidenceMetricsEntry	XPSConfidenceMetricsEntry.setRecallAt1(Float recallAt1) 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.
XPSConfidenceMetricsEntry	XPSConfidenceMetricsEntry.setTrueNegativeCount(Long trueNegativeCount) The number of labels that were not created by the model, but if they would, they would not match a ground truth label.
XPSConfidenceMetricsEntry	XPSConfidenceMetricsEntry.setTruePositiveCount(Long truePositiveCount) The number of model created labels that match a ground truth label.

Methods in com.google.api.services.language.v2.model that return types with arguments of type XPSConfidenceMetricsEntry

Modifier and Type	Method and Description
List<XPSConfidenceMetricsEntry>	XPSClassificationEvaluationMetrics.getConfidenceMetricsEntries() Metrics that have confidence thresholds.
List<XPSConfidenceMetricsEntry>	XPSTextExtractionEvaluationMetrics.getConfidenceMetricsEntries() If the enclosing EvaluationMetrics.label is empty, confidence_metrics_entries is an evaluation of the entire model across all labels.
Map<String,XPSConfidenceMetricsEntry>	XPSTextExtractionEvaluationMetrics.getPerLabelConfidenceMetrics() Only recall, precision, and f1_score will be set.

Methods in com.google.api.services.language.v2.model with parameters of type XPSConfidenceMetricsEntry

Modifier and Type	Method and Description
XPSTextExtractionEvaluationMetrics	XPSTextExtractionEvaluationMetrics.setBestF1ConfidenceMetrics(XPSConfidenceMetricsEntry bestF1ConfidenceMetrics) Values are at the highest F1 score on the precision-recall curve.

Method parameters in com.google.api.services.language.v2.model with type arguments of type XPSConfidenceMetricsEntry

Modifier and Type	Method and Description
XPSClassificationEvaluationMetrics	XPSClassificationEvaluationMetrics.setConfidenceMetricsEntries(List<XPSConfidenceMetricsEntry> confidenceMetricsEntries) Metrics that have confidence thresholds.
XPSTextExtractionEvaluationMetrics	XPSTextExtractionEvaluationMetrics.setConfidenceMetricsEntries(List<XPSConfidenceMetricsEntry> confidenceMetricsEntries) If the enclosing EvaluationMetrics.label is empty, confidence_metrics_entries is an evaluation of the entire model across all labels.
XPSTextExtractionEvaluationMetrics	XPSTextExtractionEvaluationMetrics.setPerLabelConfidenceMetrics(Map<String,XPSConfidenceMetricsEntry> perLabelConfidenceMetrics) Only recall, precision, and f1_score will be set.