Struct LearningCurveAnalysis 

pub struct LearningCurveAnalysis {
    pub data_points: Vec<DataPoint>,
    pub per_entity_curves: HashMap<String, Vec<DataPoint>>,
    pub efficiency: SampleEfficiencyMetrics,
    pub curve_fit: Option<CurveFitParams>,
    pub recommendations: Vec<String>,
}

Learning curve analysis results.

Fields

data_points: Vec<DataPoint>

Raw data points

per_entity_curves: HashMap<String, Vec<DataPoint>>

Per-entity-type learning curves (if available)

efficiency: SampleEfficiencyMetrics

Sample efficiency metrics

curve_fit: Option<CurveFitParams>

Fitted curve parameters (power law: y = a * x^b + c)

recommendations: Vec<String>

Recommendations based on curve shape

Implementations

impl LearningCurveAnalysis

pub fn samples_for_target(&self, target_f1: f64) -> Option<usize>

Estimate samples needed for a specific F1 target.

pub fn more_data_would_help(&self) -> bool

Check if more data would likely help.

Trait Implementations

impl Clone for LearningCurveAnalysis

fn clone(&self) -> LearningCurveAnalysis

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for LearningCurveAnalysis

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

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for LearningCurveAnalysis

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

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for LearningCurveAnalysis

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.