Struct aws_sdk_frauddetector::types::PredictionExplanations

#[non_exhaustive]
pub struct PredictionExplanations {
    pub variable_impact_explanations: Option<Vec<VariableImpactExplanation>>,
    pub aggregated_variables_impact_explanations: Option<Vec<AggregatedVariablesImpactExplanation>>,
}

The prediction explanations that provide insight into how each event variable impacted the model version's fraud prediction score.

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.

variable_impact_explanations: Option<Vec<VariableImpactExplanation>>

The details of the event variable's impact on the prediction score.

aggregated_variables_impact_explanations: Option<Vec<AggregatedVariablesImpactExplanation>>

The details of the aggregated variables impact on the prediction score.

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 PredictionExplanations

pub fn variable_impact_explanations(&self) -> &[VariableImpactExplanation]

The details of the event variable's impact on the prediction score.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .variable_impact_explanations.is_none().

pub fn aggregated_variables_impact_explanations( &self ) -> &[AggregatedVariablesImpactExplanation]

The details of the aggregated variables impact on the prediction score.

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.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .aggregated_variables_impact_explanations.is_none().

impl PredictionExplanations

pub fn builder() -> PredictionExplanationsBuilder

Creates a new builder-style object to manufacture PredictionExplanations.

Trait Implementations

impl Clone for PredictionExplanations

fn clone(&self) -> PredictionExplanations

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for PredictionExplanations

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

Formats the value using the given formatter.

impl PartialEq for PredictionExplanations

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