Struct aws_sdk_glue::types::builders::FindMatchesMetricsBuilder

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

A builder for FindMatchesMetrics.

Implementations

impl FindMatchesMetricsBuilder

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

The area under the precision/recall curve (AUPRC) is a single number measuring the overall quality of the transform, that is independent of the choice made for precision vs. recall. Higher values indicate that you have a more attractive precision vs. recall tradeoff.

For more information, see Precision and recall in Wikipedia.

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

The area under the precision/recall curve (AUPRC) is a single number measuring the overall quality of the transform, that is independent of the choice made for precision vs. recall. Higher values indicate that you have a more attractive precision vs. recall tradeoff.

For more information, see Precision and recall in Wikipedia.

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

The area under the precision/recall curve (AUPRC) is a single number measuring the overall quality of the transform, that is independent of the choice made for precision vs. recall. Higher values indicate that you have a more attractive precision vs. recall tradeoff.

For more information, see Precision and recall in Wikipedia.

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

The precision metric indicates when often your transform is correct when it predicts a match. Specifically, it measures how well the transform finds true positives from the total true positives possible.

For more information, see Precision and recall in Wikipedia.

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

The precision metric indicates when often your transform is correct when it predicts a match. Specifically, it measures how well the transform finds true positives from the total true positives possible.

For more information, see Precision and recall in Wikipedia.

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

The precision metric indicates when often your transform is correct when it predicts a match. Specifically, it measures how well the transform finds true positives from the total true positives possible.

For more information, see Precision and recall in Wikipedia.

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

The recall metric indicates that for an actual match, how often your transform predicts the match. Specifically, it measures how well the transform finds true positives from the total records in the source data.

For more information, see Precision and recall in Wikipedia.

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

The recall metric indicates that for an actual match, how often your transform predicts the match. Specifically, it measures how well the transform finds true positives from the total records in the source data.

For more information, see Precision and recall in Wikipedia.

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

The recall metric indicates that for an actual match, how often your transform predicts the match. Specifically, it measures how well the transform finds true positives from the total records in the source data.

For more information, see Precision and recall in Wikipedia.

pub fn f1(self, input: f64) -> Self

The maximum F1 metric indicates the transform's accuracy between 0 and 1, where 1 is the best accuracy.

For more information, see F1 score in Wikipedia.

pub fn set_f1(self, input: Option<f64>) -> Self

The maximum F1 metric indicates the transform's accuracy between 0 and 1, where 1 is the best accuracy.

For more information, see F1 score in Wikipedia.

pub fn get_f1(&self) -> &Option<f64>

The maximum F1 metric indicates the transform's accuracy between 0 and 1, where 1 is the best accuracy.

For more information, see F1 score in Wikipedia.

pub fn confusion_matrix(self, input: ConfusionMatrix) -> Self

The confusion matrix shows you what your transform is predicting accurately and what types of errors it is making.

For more information, see Confusion matrix in Wikipedia.

pub fn set_confusion_matrix(self, input: Option<ConfusionMatrix>) -> Self

The confusion matrix shows you what your transform is predicting accurately and what types of errors it is making.

For more information, see Confusion matrix in Wikipedia.

pub fn get_confusion_matrix(&self) -> &Option<ConfusionMatrix>

The confusion matrix shows you what your transform is predicting accurately and what types of errors it is making.

For more information, see Confusion matrix in Wikipedia.

pub fn column_importances(self, input: ColumnImportance) -> Self

Appends an item to column_importances.

To override the contents of this collection use set_column_importances.

A list of ColumnImportance structures containing column importance metrics, sorted in order of descending importance.

pub fn set_column_importances( self, input: Option<Vec<ColumnImportance>>, ) -> Self

A list of ColumnImportance structures containing column importance metrics, sorted in order of descending importance.

pub fn get_column_importances(&self) -> &Option<Vec<ColumnImportance>>

A list of ColumnImportance structures containing column importance metrics, sorted in order of descending importance.

pub fn build(self) -> FindMatchesMetrics

Consumes the builder and constructs a FindMatchesMetrics.

Trait Implementations

impl Clone for FindMatchesMetricsBuilder

fn clone(&self) -> FindMatchesMetricsBuilder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for FindMatchesMetricsBuilder

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

Formats the value using the given formatter.

impl Default for FindMatchesMetricsBuilder

fn default() -> FindMatchesMetricsBuilder

Returns the “default value” for a type.

impl PartialEq for FindMatchesMetricsBuilder

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