Struct aws_sdk_comprehend::types::builders::ClassifierEvaluationMetricsBuilder

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

A builder for ClassifierEvaluationMetrics.

Implementations

impl ClassifierEvaluationMetricsBuilder

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 get_accuracy(&self) -> &Option<f64>

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 get_precision(&self) -> &Option<f64>

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 get_recall(&self) -> &Option<f64>

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

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 get_micro_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. 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 get_micro_recall(&self) -> &Option<f64>

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 get_micro_f1_score(&self) -> &Option<f64>

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 get_hamming_loss(&self) -> &Option<f64>

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 ClassifierEvaluationMetricsBuilder

fn clone(&self) -> ClassifierEvaluationMetricsBuilder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for ClassifierEvaluationMetricsBuilder

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

Formats the value using the given formatter.

impl Default for ClassifierEvaluationMetricsBuilder

fn default() -> ClassifierEvaluationMetricsBuilder

Returns the “default value” for a type.

impl PartialEq for ClassifierEvaluationMetricsBuilder

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