Reference documentation and code samples for the Cloud AutoML V1 API class Google::Cloud::AutoML::V1::ClassificationEvaluationMetrics::ConfidenceMetricsEntry.
Metrics for a single confidence threshold.
Inherits
- Object
Extended By
- Google::Protobuf::MessageExts::ClassMethods
Includes
- Google::Protobuf::MessageExts
Methods
#confidence_threshold
def confidence_threshold() -> ::Float
Returns
- (::Float) — Output only. Metrics are computed with an assumption that the model never returns predictions with score lower than this value.
#confidence_threshold=
def confidence_threshold=(value) -> ::Float
Parameter
- value (::Float) — Output only. Metrics are computed with an assumption that the model never returns predictions with score lower than this value.
Returns
- (::Float) — Output only. Metrics are computed with an assumption that the model never returns predictions with score lower than this value.
#f1_score
def f1_score() -> ::Float
Returns
- (::Float) — Output only. The harmonic mean of recall and precision.
#f1_score=
def f1_score=(value) -> ::Float
Parameter
- value (::Float) — Output only. The harmonic mean of recall and precision.
Returns
- (::Float) — Output only. The harmonic mean of recall and precision.
#f1_score_at1
def f1_score_at1() -> ::Float
Returns
- (::Float) — Output only. The harmonic mean of recall_at1 and precision_at1.
#f1_score_at1=
def f1_score_at1=(value) -> ::Float
Parameter
- value (::Float) — Output only. The harmonic mean of recall_at1 and precision_at1.
Returns
- (::Float) — Output only. The harmonic mean of recall_at1 and precision_at1.
#false_negative_count
def false_negative_count() -> ::Integer
Returns
- (::Integer) — Output only. The number of ground truth labels that are not matched by a model created label.
#false_negative_count=
def false_negative_count=(value) -> ::Integer
Parameter
- value (::Integer) — Output only. The number of ground truth labels that are not matched by a model created label.
Returns
- (::Integer) — Output only. The number of ground truth labels that are not matched by a model created label.
#false_positive_count
def false_positive_count() -> ::Integer
Returns
- (::Integer) — Output only. The number of model created labels that do not match a ground truth label.
#false_positive_count=
def false_positive_count=(value) -> ::Integer
Parameter
- value (::Integer) — Output only. The number of model created labels that do not match a ground truth label.
Returns
- (::Integer) — Output only. The number of model created labels that do not match a ground truth label.
#false_positive_rate
def false_positive_rate() -> ::Float
Returns
- (::Float) — Output only. False Positive Rate for the given confidence threshold.
#false_positive_rate=
def false_positive_rate=(value) -> ::Float
Parameter
- value (::Float) — Output only. False Positive Rate for the given confidence threshold.
Returns
- (::Float) — Output only. False Positive Rate for the given confidence threshold.
#false_positive_rate_at1
def false_positive_rate_at1() -> ::Float
Returns
- (::Float) — 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.
#false_positive_rate_at1=
def false_positive_rate_at1=(value) -> ::Float
Parameter
- value (::Float) — 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.
Returns
- (::Float) — 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.
#position_threshold
def position_threshold() -> ::Integer
Returns
- (::Integer) — 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.
#position_threshold=
def position_threshold=(value) -> ::Integer
Parameter
- value (::Integer) — 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.
Returns
- (::Integer) — 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.
#precision
def precision() -> ::Float
Returns
- (::Float) — Output only. Precision for the given confidence threshold.
#precision=
def precision=(value) -> ::Float
Parameter
- value (::Float) — Output only. Precision for the given confidence threshold.
Returns
- (::Float) — Output only. Precision for the given confidence threshold.
#precision_at1
def precision_at1() -> ::Float
Returns
- (::Float) — Output only. The precision when only considering the label that has the highest prediction score and not below the confidence threshold for each example.
#precision_at1=
def precision_at1=(value) -> ::Float
Parameter
- value (::Float) — Output only. The precision when only considering the label that has the highest prediction score and not below the confidence threshold for each example.
Returns
- (::Float) — Output only. The precision when only considering the label that has the highest prediction score and not below the confidence threshold for each example.
#recall
def recall() -> ::Float
Returns
- (::Float) — Output only. Recall (True Positive Rate) for the given confidence threshold.
#recall=
def recall=(value) -> ::Float
Parameter
- value (::Float) — Output only. Recall (True Positive Rate) for the given confidence threshold.
Returns
- (::Float) — Output only. Recall (True Positive Rate) for the given confidence threshold.
#recall_at1
def recall_at1() -> ::Float
Returns
- (::Float) — 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.
#recall_at1=
def recall_at1=(value) -> ::Float
Parameter
- value (::Float) — 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.
Returns
- (::Float) — 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.
#true_negative_count
def true_negative_count() -> ::Integer
Returns
- (::Integer) — 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.
#true_negative_count=
def true_negative_count=(value) -> ::Integer
Parameter
- value (::Integer) — 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.
Returns
- (::Integer) — 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.
#true_positive_count
def true_positive_count() -> ::Integer
Returns
- (::Integer) — Output only. The number of model created labels that match a ground truth label.
#true_positive_count=
def true_positive_count=(value) -> ::Integer
Parameter
- value (::Integer) — Output only. The number of model created labels that match a ground truth label.
Returns
- (::Integer) — Output only. The number of model created labels that match a ground truth label.