Struct gapi_grpc::google::cloud::automl::v1::classification_evaluation_metrics::ConfidenceMetricsEntry[][src]

pub struct ConfidenceMetricsEntry {
    pub confidence_threshold: f32,
    pub position_threshold: i32,
    pub recall: f32,
    pub precision: f32,
    pub false_positive_rate: f32,
    pub f1_score: f32,
    pub recall_at1: f32,
    pub precision_at1: f32,
    pub false_positive_rate_at1: f32,
    pub f1_score_at1: f32,
    pub true_positive_count: i64,
    pub false_positive_count: i64,
    pub false_negative_count: i64,
    pub true_negative_count: i64,
}

Metrics for a single confidence threshold.

Fields

confidence_threshold: f32

Output only. Metrics are computed with an assumption that the model never returns predictions with score lower than this value.

position_threshold: i32

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.

recall: f32

Output only. Recall (True Positive Rate) for the given confidence threshold.

precision: f32

Output only. Precision for the given confidence threshold.

false_positive_rate: f32

Output only. False Positive Rate for the given confidence threshold.

f1_score: f32

Output only. The harmonic mean of recall and precision.

recall_at1: f32

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.

precision_at1: f32

Output only. The precision when only considering the label that has the highest prediction score and not below the confidence threshold for each example.

false_positive_rate_at1: f32

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.

f1_score_at1: f32

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].

true_positive_count: i64

Output only. The number of model created labels that match a ground truth label.

false_positive_count: i64

Output only. The number of model created labels that do not match a ground truth label.

false_negative_count: i64

Output only. The number of ground truth labels that are not matched by a model created label.

true_negative_count: i64

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.

Trait Implementations

impl Clone for ConfidenceMetricsEntry[src]

impl Debug for ConfidenceMetricsEntry[src]

impl Default for ConfidenceMetricsEntry[src]

impl Message for ConfidenceMetricsEntry[src]

impl PartialEq<ConfidenceMetricsEntry> for ConfidenceMetricsEntry[src]

impl StructuralPartialEq for ConfidenceMetricsEntry[src]

Auto Trait Implementations

impl RefUnwindSafe for ConfidenceMetricsEntry

impl Send for ConfidenceMetricsEntry

impl Sync for ConfidenceMetricsEntry

impl Unpin for ConfidenceMetricsEntry

impl UnwindSafe for ConfidenceMetricsEntry

Blanket Implementations

impl<T> Any for T where
    T: 'static + ?Sized
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impl<T> Borrow<T> for T where
    T: ?Sized
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impl<T> BorrowMut<T> for T where
    T: ?Sized
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impl<T> From<T> for T[src]

impl<T> Instrument for T[src]

impl<T> Instrument for T[src]

impl<T, U> Into<U> for T where
    U: From<T>, 
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impl<T> IntoRequest<T> for T[src]

impl<T> ToOwned for T where
    T: Clone
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type Owned = T

The resulting type after obtaining ownership.

impl<T, U> TryFrom<U> for T where
    U: Into<T>, 
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type Error = Infallible

The type returned in the event of a conversion error.

impl<T, U> TryInto<U> for T where
    U: TryFrom<T>, 
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type Error = <U as TryFrom<T>>::Error

The type returned in the event of a conversion error.

impl<V, T> VZip<V> for T where
    V: MultiLane<T>, 
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impl<T> WithSubscriber for T[src]