Struct aws_sdk_machinelearning::model::PerformanceMetrics

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

Measurements of how well the MLModel performed on known observations. One of the following metrics is returned, based on the type of the MLModel:

• BinaryAUC: The binary MLModel uses the Area Under the Curve (AUC) technique to measure performance.

• RegressionRMSE: The regression MLModel uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.

• MulticlassAvgFScore: The multiclass MLModel uses the F1 score technique to measure performance.

For more information about performance metrics, please see the Amazon Machine Learning Developer Guide.

Implementations

impl PerformanceMetrics

pub fn properties(&self) -> Option<&HashMap<String, String>>

impl PerformanceMetrics

pub fn builder() -> Builder

Creates a new builder-style object to manufacture PerformanceMetrics.

Trait Implementations

impl Clone for PerformanceMetrics

fn clone(&self) -> PerformanceMetrics

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for PerformanceMetrics

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

Formats the value using the given formatter.

impl PartialEq<PerformanceMetrics> for PerformanceMetrics

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