Struct BenchmarkRegressor 

pub struct BenchmarkRegressor { /* private fields */ }

Benchmark regressor implementing standard ML baselines

Implementations

impl BenchmarkRegressor

pub fn new(strategy: BenchmarkStrategy) -> Self

Create a new benchmark regressor

pub fn with_random_state(self, seed: u64) -> Self

Set random state for reproducible results

Trait Implementations

impl Clone for BenchmarkRegressor

fn clone(&self) -> BenchmarkRegressor

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for BenchmarkRegressor

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

Formats the value using the given formatter.

impl Estimator for BenchmarkRegressor

type Config = BenchmarkStrategy

Configuration type for the estimator

type Error = SklearsError

Error type for the estimator

type Float = f64

The numeric type used by this estimator

fn config(&self) -> &Self::Config

Get estimator configuration

fn validate_config(&self) -> Result<(), SklearsError>

Validate estimator configuration with detailed error context

fn check_compatibility( &self, n_samples: usize, n_features: usize, ) -> Result<(), SklearsError>

Check if estimator is compatible with given data dimensions

fn metadata(&self) -> EstimatorMetadata

Get estimator metadata

impl Fit<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>> for BenchmarkRegressor

type Fitted = TrainedBenchmarkRegressor

The fitted model type

fn fit( self, x: &Array2<f64>, y: &Array1<f64>, ) -> Result<Self::Fitted, SklearsError>

Fit the model to the provided data with validation

fn fit_with_validation( self, x: &X, y: &Y, _x_val: Option<&X>, _y_val: Option<&Y>, ) -> Result<(Self::Fitted, FitMetrics), SklearsError>

where Self: Sized,

Fit with custom validation and early stopping