Struct GridSearchCV 

pub struct GridSearchCV<E, F, ConfigFn>
where
    E: Clone + Fit<Array2<Float>, Array1<Float>, Fitted = F>,
    F: Predict<Array2<Float>, Array1<Float>> + Score<Array2<Float>, Array1<Float>, Float = f64>,
    ConfigFn: Fn(E, &ParameterSet) -> Result<E>,
{ /* private fields */ }

Grid search cross-validation

Exhaustive search over specified parameter values for an estimator. Uses cross-validation to evaluate each parameter combination.

Implementations

impl<E, F, ConfigFn> GridSearchCV<E, F, ConfigFn>

where E: Clone + Fit<Array2<Float>, Array1<Float>, Fitted = F>, F: Predict<Array2<Float>, Array1<Float>> + Score<Array2<Float>, Array1<Float>, Float = f64>, ConfigFn: Fn(E, &ParameterSet) -> Result<E>,

pub fn new(estimator: E, param_grid: ParameterGrid, config_fn: ConfigFn) -> Self

Create a new grid search CV

pub fn cv<C: CrossValidator + 'static>(self, cv: C) -> Self

Set the cross-validation strategy

pub fn scoring(self, scoring: Scoring) -> Self

Set the scoring method

pub fn n_jobs(self, n_jobs: Option<usize>) -> Self

Set the number of parallel jobs

pub fn refit(self, refit: bool) -> Self

Set whether to refit with best parameters

pub fn best_estimator(&self) -> Option<&F>

Get the best estimator (after fitting)

pub fn best_params(&self) -> Option<&ParameterSet>

Get the best parameters (after fitting)

pub fn best_score(&self) -> Option<f64>

Get the best score (after fitting)

pub fn cv_results(&self) -> Option<&GridSearchResults>

Get the CV results (after fitting)

Trait Implementations

impl<E, F, ConfigFn> Fit<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>> for GridSearchCV<E, F, ConfigFn>

where E: Clone + Send + Sync + Fit<Array2<Float>, Array1<Float>, Fitted = F>, F: Predict<Array2<Float>, Array1<Float>> + Send + Sync + Score<Array2<Float>, Array1<Float>, Float = f64>, ConfigFn: Fn(E, &ParameterSet) -> Result<E> + Send + Sync,

type Fitted = GridSearchCV<E, F, ConfigFn>

The fitted model type

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

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

impl<E, F, ConfigFn> Predict<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>> for GridSearchCV<E, F, ConfigFn>

where E: Clone + Fit<Array2<Float>, Array1<Float>, Fitted = F>, F: Predict<Array2<Float>, Array1<Float>> + Score<Array2<Float>, Array1<Float>, Float = f64>, ConfigFn: Fn(E, &ParameterSet) -> Result<E>,

fn predict(&self, x: &Array2<Float>) -> Result<Array1<Float>>

Make predictions on the provided data

fn predict_with_uncertainty( &self, x: &X, ) -> Result<(Output, UncertaintyMeasure), SklearsError>

Make predictions with confidence intervals

impl<E, F, ConfigFn> Score<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>> for GridSearchCV<E, F, ConfigFn>

where E: Clone + Fit<Array2<Float>, Array1<Float>, Fitted = F>, F: Predict<Array2<Float>, Array1<Float>> + Score<Array2<Float>, Array1<Float>, Float = f64>, ConfigFn: Fn(E, &ParameterSet) -> Result<E>,

type Float = f64

The numeric type for score calculation

fn score(&self, x: &Array2<Float>, y: &Array1<Float>) -> Result<f64>

Calculate the score of the model on the provided data