Struct aws_sdk_comprehend::types::EntityRecognizerEvaluationMetrics

#[non_exhaustive]
pub struct EntityRecognizerEvaluationMetrics {
    pub precision: Option<f64>,
    pub recall: Option<f64>,
    pub f1_score: Option<f64>,
}

Detailed information about the accuracy of an entity recognizer.

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<f64>

A measure of the usefulness of the recognizer results in the test data. High precision means that the recognizer returned substantially more relevant results than irrelevant ones.

recall: Option<f64>

A measure of how complete the recognizer results are for the test data. High recall means that the recognizer returned most of the relevant results.

f1_score: Option<f64>

A measure of how accurate the recognizer results are for the test data. It is derived from the Precision and Recall values. The F1Score is the harmonic average of the two scores. For plain text entity recognizer models, the range is 0 to 100, where 100 is the best score. For PDF/Word entity recognizer models, the range is 0 to 1, where 1 is the best score.

Implementations

impl EntityRecognizerEvaluationMetrics

pub fn precision(&self) -> Option<f64>

A measure of the usefulness of the recognizer results in the test data. High precision means that the recognizer returned substantially more relevant results than irrelevant ones.

pub fn recall(&self) -> Option<f64>

A measure of how complete the recognizer results are for the test data. High recall means that the recognizer returned most of the relevant results.

pub fn f1_score(&self) -> Option<f64>

A measure of how accurate the recognizer results are for the test data. It is derived from the Precision and Recall values. The F1Score is the harmonic average of the two scores. For plain text entity recognizer models, the range is 0 to 100, where 100 is the best score. For PDF/Word entity recognizer models, the range is 0 to 1, where 1 is the best score.

impl EntityRecognizerEvaluationMetrics

pub fn builder() -> EntityRecognizerEvaluationMetricsBuilder

Creates a new builder-style object to manufacture EntityRecognizerEvaluationMetrics.

Trait Implementations

impl Clone for EntityRecognizerEvaluationMetrics

fn clone(&self) -> EntityRecognizerEvaluationMetrics

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for EntityRecognizerEvaluationMetrics

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

Formats the value using the given formatter.

impl PartialEq for EntityRecognizerEvaluationMetrics

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

This method tests for self and other values to be equal, and is used by ==.

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

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for EntityRecognizerEvaluationMetrics