Struct aws_sdk_comprehend::types::builders::EntityRecognizerEvaluationMetricsBuilder

#[non_exhaustive]
pub struct EntityRecognizerEvaluationMetricsBuilder { /* private fields */ }

A builder for EntityRecognizerEvaluationMetrics.

Implementations

impl EntityRecognizerEvaluationMetricsBuilder

pub fn precision(self, input: f64) -> Self

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 set_precision(self, input: Option<f64>) -> Self

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 get_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, input: f64) -> Self

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 set_recall(self, input: Option<f64>) -> Self

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 get_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, input: f64) -> Self

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.

pub fn set_f1_score(self, input: Option<f64>) -> Self

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.

pub fn get_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.

pub fn build(self) -> EntityRecognizerEvaluationMetrics

Consumes the builder and constructs a EntityRecognizerEvaluationMetrics.

Trait Implementations

impl Clone for EntityRecognizerEvaluationMetricsBuilder

fn clone(&self) -> EntityRecognizerEvaluationMetricsBuilder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for EntityRecognizerEvaluationMetricsBuilder

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

Formats the value using the given formatter.

impl Default for EntityRecognizerEvaluationMetricsBuilder

fn default() -> EntityRecognizerEvaluationMetricsBuilder

Returns the “default value” for a type.

impl PartialEq for EntityRecognizerEvaluationMetricsBuilder

fn eq(&self, other: &EntityRecognizerEvaluationMetricsBuilder) -> 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 EntityRecognizerEvaluationMetricsBuilder