Struct aws_sdk_machinelearning::model::PerformanceMetrics

#[non_exhaustive]
pub struct PerformanceMetrics {
    pub properties: Option<HashMap<String, String>>,
}

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.

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.

properties: Option<HashMap<String, String>>

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: &PerformanceMetrics) -> bool

This method tests for !=.

impl StructuralPartialEq for PerformanceMetrics