Struct aws_sdk_comprehend::types::builders::EntityTypesEvaluationMetricsBuilder

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

A builder for EntityTypesEvaluationMetrics.

Implementations

impl EntityTypesEvaluationMetricsBuilder

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

A measure of the usefulness of the recognizer results for a specific entity type 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 for a specific entity type 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 for a specific entity type 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 a specific entity type in 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 a specific entity type in 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 a specific entity type in 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 a specific entity type in the test data. It is derived from the Precision and Recall values. The F1Score is the harmonic average of the two scores. The highest score is 1, and the worst score is 0.

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

A measure of how accurate the recognizer results are for a specific entity type in the test data. It is derived from the Precision and Recall values. The F1Score is the harmonic average of the two scores. The highest score is 1, and the worst score is 0.

pub fn get_f1_score(&self) -> &Option<f64>

A measure of how accurate the recognizer results are for a specific entity type in the test data. It is derived from the Precision and Recall values. The F1Score is the harmonic average of the two scores. The highest score is 1, and the worst score is 0.

pub fn build(self) -> EntityTypesEvaluationMetrics

Consumes the builder and constructs a EntityTypesEvaluationMetrics.

Trait Implementations

impl Clone for EntityTypesEvaluationMetricsBuilder

fn clone(&self) -> EntityTypesEvaluationMetricsBuilder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for EntityTypesEvaluationMetricsBuilder

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

Formats the value using the given formatter.

impl Default for EntityTypesEvaluationMetricsBuilder

fn default() -> EntityTypesEvaluationMetricsBuilder

Returns the “default value” for a type.

impl PartialEq for EntityTypesEvaluationMetricsBuilder

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

Tests for self and other values to be equal, and is used by ==.

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

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

impl StructuralPartialEq for EntityTypesEvaluationMetricsBuilder