Struct aws_sdk_glue::types::FindMatchesMetrics

#[non_exhaustive]
pub struct FindMatchesMetrics {
    pub area_under_pr_curve: Option<f64>,
    pub precision: Option<f64>,
    pub recall: Option<f64>,
    pub f1: Option<f64>,
    pub confusion_matrix: Option<ConfusionMatrix>,
    pub column_importances: Option<Vec<ColumnImportance>>,
}

The evaluation metrics for the find matches algorithm. The quality of your machine learning transform is measured by getting your transform to predict some matches and comparing the results to known matches from the same dataset. The quality metrics are based on a subset of your data, so they are not precise.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

area_under_pr_curve: 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.

precision: 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.

recall: 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.

f1: 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.

confusion_matrix: 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.

column_importances: Option<Vec<ColumnImportance>>

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

Implementations

impl FindMatchesMetrics

pub fn 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) -> 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) -> 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) -> 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) -> 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) -> &[ColumnImportance]

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

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .column_importances.is_none().

impl FindMatchesMetrics

pub fn builder() -> FindMatchesMetricsBuilder

Creates a new builder-style object to manufacture FindMatchesMetrics.

Trait Implementations

impl Clone for FindMatchesMetrics

fn clone(&self) -> FindMatchesMetrics

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for FindMatchesMetrics

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

Formats the value using the given formatter.

impl PartialEq for FindMatchesMetrics

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