Struct aws_sdk_frauddetector::types::TrainingResultV2

#[non_exhaustive]
pub struct TrainingResultV2 {
    pub data_validation_metrics: Option<DataValidationMetrics>,
    pub training_metrics_v2: Option<TrainingMetricsV2>,
    pub variable_importance_metrics: Option<VariableImportanceMetrics>,
    pub aggregated_variables_importance_metrics: Option<AggregatedVariablesImportanceMetrics>,
}

The training result details.

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.

data_validation_metrics: Option<DataValidationMetrics>

The model training data validation metrics.

training_metrics_v2: Option<TrainingMetricsV2>

The training metric details.

variable_importance_metrics: Option<VariableImportanceMetrics>

The variable importance metrics details.

aggregated_variables_importance_metrics: Option<AggregatedVariablesImportanceMetrics>

The variable importance metrics of the aggregated variables.

Account Takeover Insights (ATI) model uses event variables from the login data you provide to continuously calculate a set of variables (aggregated variables) based on historical events. For example, your ATI model might calculate the number of times an user has logged in using the same IP address. In this case, event variables used to derive the aggregated variables are IP address and user.

Implementations

impl TrainingResultV2

pub fn data_validation_metrics(&self) -> Option<&DataValidationMetrics>

The model training data validation metrics.

pub fn training_metrics_v2(&self) -> Option<&TrainingMetricsV2>

The training metric details.

pub fn variable_importance_metrics(&self) -> Option<&VariableImportanceMetrics>

The variable importance metrics details.

pub fn aggregated_variables_importance_metrics( &self ) -> Option<&AggregatedVariablesImportanceMetrics>

The variable importance metrics of the aggregated variables.

Account Takeover Insights (ATI) model uses event variables from the login data you provide to continuously calculate a set of variables (aggregated variables) based on historical events. For example, your ATI model might calculate the number of times an user has logged in using the same IP address. In this case, event variables used to derive the aggregated variables are IP address and user.

impl TrainingResultV2

pub fn builder() -> TrainingResultV2Builder

Creates a new builder-style object to manufacture TrainingResultV2.

Trait Implementations

impl Clone for TrainingResultV2

fn clone(&self) -> TrainingResultV2

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for TrainingResultV2

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

Formats the value using the given formatter.

impl PartialEq for TrainingResultV2

fn eq(&self, other: &TrainingResultV2) -> 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 TrainingResultV2