Struct AggregateClassificationMetrics 

#[non_exhaustive]
pub struct AggregateClassificationMetrics {
    pub precision: Option<DoubleValue>,
    pub recall: Option<DoubleValue>,
    pub accuracy: Option<DoubleValue>,
    pub threshold: Option<DoubleValue>,
    pub f1_score: Option<DoubleValue>,
    pub log_loss: Option<DoubleValue>,
    pub roc_auc: Option<DoubleValue>,
    /* private fields */
}

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.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

precision: Option<DoubleValue>

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.

recall: Option<DoubleValue>

Recall is the fraction of actual positive labels that were given a positive prediction. For multiclass this is a macro-averaged metric.

accuracy: Option<DoubleValue>

Accuracy is the fraction of predictions given the correct label. For multiclass this is a micro-averaged metric.

threshold: Option<DoubleValue>

Threshold at which the metrics are computed. For binary classification models this is the positive class threshold. For multi-class classification models this is the confidence threshold.

f1_score: Option<DoubleValue>

The F1 score is an average of recall and precision. For multiclass this is a macro-averaged metric.

log_loss: Option<DoubleValue>

Logarithmic Loss. For multiclass this is a macro-averaged metric.

roc_auc: Option<DoubleValue>

Area Under a ROC Curve. For multiclass this is a macro-averaged metric.

Implementations

impl AggregateClassificationMetrics

pub fn new() -> Self

Creates a new default instance.

pub fn set_precision<T>(self, v: T) -> Self

where T: Into<DoubleValue>,

Sets the value of precision.

Example

use wkt::DoubleValue;
let x = AggregateClassificationMetrics::new().set_precision(DoubleValue::default()/* use setters */);

pub fn set_or_clear_precision<T>(self, v: Option<T>) -> Self

where T: Into<DoubleValue>,

Sets or clears the value of precision.

Example

use wkt::DoubleValue;
let x = AggregateClassificationMetrics::new().set_or_clear_precision(Some(DoubleValue::default()/* use setters */));
let x = AggregateClassificationMetrics::new().set_or_clear_precision(None::<DoubleValue>);

pub fn set_recall<T>(self, v: T) -> Self

where T: Into<DoubleValue>,

Sets the value of recall.

Example

use wkt::DoubleValue;
let x = AggregateClassificationMetrics::new().set_recall(DoubleValue::default()/* use setters */);

pub fn set_or_clear_recall<T>(self, v: Option<T>) -> Self

where T: Into<DoubleValue>,

Sets or clears the value of recall.

Example

use wkt::DoubleValue;
let x = AggregateClassificationMetrics::new().set_or_clear_recall(Some(DoubleValue::default()/* use setters */));
let x = AggregateClassificationMetrics::new().set_or_clear_recall(None::<DoubleValue>);

pub fn set_accuracy<T>(self, v: T) -> Self

where T: Into<DoubleValue>,

Sets the value of accuracy.

Example

use wkt::DoubleValue;
let x = AggregateClassificationMetrics::new().set_accuracy(DoubleValue::default()/* use setters */);

pub fn set_or_clear_accuracy<T>(self, v: Option<T>) -> Self

where T: Into<DoubleValue>,

Sets or clears the value of accuracy.

Example

use wkt::DoubleValue;
let x = AggregateClassificationMetrics::new().set_or_clear_accuracy(Some(DoubleValue::default()/* use setters */));
let x = AggregateClassificationMetrics::new().set_or_clear_accuracy(None::<DoubleValue>);

pub fn set_threshold<T>(self, v: T) -> Self

where T: Into<DoubleValue>,

Sets the value of threshold.

Example

use wkt::DoubleValue;
let x = AggregateClassificationMetrics::new().set_threshold(DoubleValue::default()/* use setters */);

pub fn set_or_clear_threshold<T>(self, v: Option<T>) -> Self

where T: Into<DoubleValue>,

Sets or clears the value of threshold.

Example

use wkt::DoubleValue;
let x = AggregateClassificationMetrics::new().set_or_clear_threshold(Some(DoubleValue::default()/* use setters */));
let x = AggregateClassificationMetrics::new().set_or_clear_threshold(None::<DoubleValue>);

pub fn set_f1_score<T>(self, v: T) -> Self

where T: Into<DoubleValue>,

Sets the value of f1_score.

Example

use wkt::DoubleValue;
let x = AggregateClassificationMetrics::new().set_f1_score(DoubleValue::default()/* use setters */);

pub fn set_or_clear_f1_score<T>(self, v: Option<T>) -> Self

where T: Into<DoubleValue>,

Sets or clears the value of f1_score.

Example

use wkt::DoubleValue;
let x = AggregateClassificationMetrics::new().set_or_clear_f1_score(Some(DoubleValue::default()/* use setters */));
let x = AggregateClassificationMetrics::new().set_or_clear_f1_score(None::<DoubleValue>);

pub fn set_log_loss<T>(self, v: T) -> Self

where T: Into<DoubleValue>,

Sets the value of log_loss.

Example

use wkt::DoubleValue;
let x = AggregateClassificationMetrics::new().set_log_loss(DoubleValue::default()/* use setters */);

pub fn set_or_clear_log_loss<T>(self, v: Option<T>) -> Self

where T: Into<DoubleValue>,

Sets or clears the value of log_loss.

Example

use wkt::DoubleValue;
let x = AggregateClassificationMetrics::new().set_or_clear_log_loss(Some(DoubleValue::default()/* use setters */));
let x = AggregateClassificationMetrics::new().set_or_clear_log_loss(None::<DoubleValue>);

pub fn set_roc_auc<T>(self, v: T) -> Self

where T: Into<DoubleValue>,

Sets the value of roc_auc.

Example

use wkt::DoubleValue;
let x = AggregateClassificationMetrics::new().set_roc_auc(DoubleValue::default()/* use setters */);

pub fn set_or_clear_roc_auc<T>(self, v: Option<T>) -> Self

where T: Into<DoubleValue>,

Sets or clears the value of roc_auc.

Example

use wkt::DoubleValue;
let x = AggregateClassificationMetrics::new().set_or_clear_roc_auc(Some(DoubleValue::default()/* use setters */));
let x = AggregateClassificationMetrics::new().set_or_clear_roc_auc(None::<DoubleValue>);

Trait Implementations

impl Clone for AggregateClassificationMetrics

fn clone(&self) -> Self

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for AggregateClassificationMetrics

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for AggregateClassificationMetrics

fn default() -> Self

Returns the “default value” for a type.

impl Message for AggregateClassificationMetrics

fn typename() -> &'static str

The typename of this message.

impl PartialEq for AggregateClassificationMetrics

fn eq(&self, other: &Self) -> bool

Equality operator ==.

fn ne(&self, other: &Rhs) -> bool

Inequality operator !=.

impl StructuralPartialEq for AggregateClassificationMetrics