Struct PerformanceMetrics

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

properties: Option<HashMap<String, String>>

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 Default for PerformanceMetrics

fn default() -> PerformanceMetrics

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for PerformanceMetrics

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl PartialEq for PerformanceMetrics

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

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for PerformanceMetrics