Struct aws_sdk_comprehend::model::classifier_evaluation_metrics::Builder

#[non_exhaustive]
pub struct Builder { /* fields omitted */ }

A builder for ClassifierEvaluationMetrics

Implementations

impl Builder

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

The fraction of the labels that were correct recognized. It is computed by dividing the number of labels in the test documents that were correctly recognized by the total number of labels in the test documents.

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

The fraction of the labels that were correct recognized. It is computed by dividing the number of labels in the test documents that were correctly recognized by the total number of labels in the test documents.

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

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

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

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

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

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

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

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

pub fn f1_score(self, input: f64) -> Self

A measure of how accurate the classifier 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. 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 classifier 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. The highest score is 1, and the worst score is 0.

pub fn micro_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. Unlike the Precision metric which comes from averaging the precision of all available labels, this is based on the overall score of all precision scores added together.

pub fn set_micro_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. Unlike the Precision metric which comes from averaging the precision of all available labels, this is based on the overall score of all precision scores added together.

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

A measure of how complete the classifier results are for the test data. High recall means that the classifier returned most of the relevant results. Specifically, this indicates how many of the correct categories in the text that the model can predict. It is a percentage of correct categories in the text that can found. Instead of averaging the recall scores of all labels (as with Recall), micro Recall is based on the overall score of all recall scores added together.

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

A measure of how complete the classifier results are for the test data. High recall means that the classifier returned most of the relevant results. Specifically, this indicates how many of the correct categories in the text that the model can predict. It is a percentage of correct categories in the text that can found. Instead of averaging the recall scores of all labels (as with Recall), micro Recall is based on the overall score of all recall scores added together.

pub fn micro_f1_score(self, input: f64) -> Self

A measure of how accurate the classifier results are for the test data. It is a combination of the Micro Precision and Micro Recall values. The Micro F1Score is the harmonic mean of the two scores. The highest score is 1, and the worst score is 0.

pub fn set_micro_f1_score(self, input: Option<f64>) -> Self

A measure of how accurate the classifier results are for the test data. It is a combination of the Micro Precision and Micro Recall values. The Micro F1Score is the harmonic mean of the two scores. The highest score is 1, and the worst score is 0.

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

Indicates the fraction of labels that are incorrectly predicted. Also seen as the fraction of wrong labels compared to the total number of labels. Scores closer to zero are better.

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

Indicates the fraction of labels that are incorrectly predicted. Also seen as the fraction of wrong labels compared to the total number of labels. Scores closer to zero are better.

pub fn build(self) -> ClassifierEvaluationMetrics

Consumes the builder and constructs a ClassifierEvaluationMetrics

Trait Implementations

impl Clone for Builder

fn clone(&self) -> Builder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for Builder

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

Formats the value using the given formatter.

impl Default for Builder

fn default() -> Builder

Returns the “default value” for a type.

impl PartialEq<Builder> for Builder

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

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

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

This method tests for !=.

impl StructuralPartialEq for Builder