Struct aws_sdk_frauddetector::types::builders::TrainingResultV2Builder

#[non_exhaustive]
pub struct TrainingResultV2Builder { /* private fields */ }

A builder for TrainingResultV2.

Implementations

impl TrainingResultV2Builder

pub fn data_validation_metrics(self, input: DataValidationMetrics) -> Self

The model training data validation metrics.

pub fn set_data_validation_metrics( self, input: Option<DataValidationMetrics> ) -> Self

The model training data validation metrics.

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

The model training data validation metrics.

pub fn training_metrics_v2(self, input: TrainingMetricsV2) -> Self

The training metric details.

pub fn set_training_metrics_v2(self, input: Option<TrainingMetricsV2>) -> Self

The training metric details.

pub fn get_training_metrics_v2(&self) -> &Option<TrainingMetricsV2>

The training metric details.

pub fn variable_importance_metrics( self, input: VariableImportanceMetrics ) -> Self

The variable importance metrics details.

pub fn set_variable_importance_metrics( self, input: Option<VariableImportanceMetrics> ) -> Self

The variable importance metrics details.

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

The variable importance metrics details.

pub fn aggregated_variables_importance_metrics( self, input: AggregatedVariablesImportanceMetrics ) -> Self

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.

pub fn set_aggregated_variables_importance_metrics( self, input: Option<AggregatedVariablesImportanceMetrics> ) -> Self

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.

pub fn get_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.

pub fn build(self) -> TrainingResultV2

Consumes the builder and constructs a TrainingResultV2.

Trait Implementations

impl Clone for TrainingResultV2Builder

fn clone(&self) -> TrainingResultV2Builder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for TrainingResultV2Builder

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

Formats the value using the given formatter.

impl Default for TrainingResultV2Builder

fn default() -> TrainingResultV2Builder

Returns the “default value” for a type.

impl PartialEq for TrainingResultV2Builder

fn eq(&self, other: &TrainingResultV2Builder) -> 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 TrainingResultV2Builder