[−][src]Struct google_bigquery2::AggregateClassificationMetrics
Aggregate metrics for classification/classifier models. For multi-class models, the metrics are either macro-averaged or micro-averaged. When macro-averaged, the metrics are calculated for each label and then an unweighted average is taken of those values. When micro-averaged, the metric is calculated globally by counting the total number of correctly predicted rows.
This type is not used in any activity, and only used as part of another schema.
Fields
log_loss: Option<f64>
Logarithmic Loss. For multiclass this is a macro-averaged metric.
threshold: Option<f64>
Threshold at which the metrics are computed. For binary classification models this is the positive class threshold. For multi-class classfication models this is the confidence threshold.
recall: Option<f64>
Recall is the fraction of actual positive labels that were given a positive prediction. For multiclass this is a macro-averaged metric.
roc_auc: Option<f64>
Area Under a ROC Curve. For multiclass this is a macro-averaged metric.
f1_score: Option<f64>
The F1 score is an average of recall and precision. For multiclass this is a macro-averaged metric.
precision: Option<f64>
Precision is the fraction of actual positive predictions that had positive actual labels. For multiclass this is a macro-averaged metric treating each class as a binary classifier.
accuracy: Option<f64>
Accuracy is the fraction of predictions given the correct label. For multiclass this is a micro-averaged metric.
Trait Implementations
impl Clone for AggregateClassificationMetrics
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fn clone(&self) -> AggregateClassificationMetrics
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fn clone_from(&mut self, source: &Self)
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impl Debug for AggregateClassificationMetrics
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impl Default for AggregateClassificationMetrics
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impl<'de> Deserialize<'de> for AggregateClassificationMetrics
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fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
__D: Deserializer<'de>,
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__D: Deserializer<'de>,
impl Part for AggregateClassificationMetrics
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impl Serialize for AggregateClassificationMetrics
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Auto Trait Implementations
impl RefUnwindSafe for AggregateClassificationMetrics
impl Send for AggregateClassificationMetrics
impl Sync for AggregateClassificationMetrics
impl Unpin for AggregateClassificationMetrics
impl UnwindSafe for AggregateClassificationMetrics
Blanket Implementations
impl<T> Any for T where
T: 'static + ?Sized,
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T: 'static + ?Sized,
impl<T> Borrow<T> for T where
T: ?Sized,
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T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
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T: ?Sized,
fn borrow_mut(&mut self) -> &mut T
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impl<T> DeserializeOwned for T where
T: for<'de> Deserialize<'de>,
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T: for<'de> Deserialize<'de>,
impl<T> From<T> for T
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impl<T, U> Into<U> for T where
U: From<T>,
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U: From<T>,
impl<T> ToOwned for T where
T: Clone,
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T: Clone,
type Owned = T
The resulting type after obtaining ownership.
fn to_owned(&self) -> T
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fn clone_into(&self, target: &mut T)
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impl<T, U> TryFrom<U> for T where
U: Into<T>,
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U: Into<T>,
type Error = Infallible
The type returned in the event of a conversion error.
fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>
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impl<T, U> TryInto<U> for T where
U: TryFrom<T>,
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U: TryFrom<T>,
type Error = <U as TryFrom<T>>::Error
The type returned in the event of a conversion error.
fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>
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impl<T> Typeable for T where
T: Any,
T: Any,