Function validation_curve 

pub fn validation_curve<E, F, C>(
    estimator: E,
    x: &Array2<Float>,
    y: &Array1<Float>,
    _param_name: &str,
    param_range: Vec<ParameterValue>,
    param_config: ParamConfigFn<E>,
    cv: &C,
    scoring: Option<Scoring>,
    confidence_level: Option<f64>,
) -> Result<ValidationCurveResult>
where
    E: Clone + Fit<Array2<Float>, Array1<Float>, Fitted = F>,
    F: Clone + Predict<Array2<Float>, Array1<Float>> + Score<Array2<Float>, Array1<Float>, Float = f64>,
    C: CrossValidator,

Compute validation curves for an estimator

Determines training and test scores for a varying parameter value. This is useful to understand the effect of a specific parameter on model performance and to detect overfitting/underfitting.

Arguments

• estimator - The estimator to evaluate
• x - Training data features
• y - Training data targets
• _param_name - Name of the parameter being varied (for documentation)
• param_range - Parameter values to test
• param_config - Function to configure estimator with parameter values
• cv - Cross-validation splitter
• scoring - Scoring method to use
• confidence_level - Confidence level for error bars (default: 0.95 for 95% confidence interval)