Struct FixedThresholdClassifier 

pub struct FixedThresholdClassifier<E> { /* private fields */ }

Fixed threshold classifier wrapper

Applies a fixed decision threshold to a probabilistic classifier’s predictions.

Implementations

impl<E> FixedThresholdClassifier<E>

pub fn new(estimator: E, threshold: f64) -> Self

Create a new fixed threshold classifier

pub fn threshold(self, threshold: f64) -> Self

Set the threshold value

pub fn pos_label_idx(self, idx: usize) -> Self

Set the positive label index for multiclass

pub fn get_threshold(&self) -> f64

Get the threshold value

pub fn estimator(&self) -> &E

Get the base estimator

Trait Implementations

impl<E: Clone> Clone for FixedThresholdClassifier<E>

fn clone(&self) -> FixedThresholdClassifier<E>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<E: Debug> Debug for FixedThresholdClassifier<E>

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

Formats the value using the given formatter.

impl<'a, E, F: FloatBounds> Fit<ArrayBase<ViewRepr<&'a F>, Dim<[usize; 2]>>, ArrayBase<ViewRepr<&'a usize>, Dim<[usize; 1]>>> for FixedThresholdClassifier<E>

where E: Fit<ArrayView2<'a, F>, ArrayView1<'a, usize>>,

type Fitted = FixedThresholdClassifier<<E as Fit<ArrayBase<ViewRepr<&'a F>, Dim<[usize; 2]>>, ArrayBase<ViewRepr<&'a usize>, Dim<[usize; 1]>>>>::Fitted>

The fitted model type

fn fit( self, x: &ArrayView2<'a, F>, y: &ArrayView1<'a, usize>, ) -> 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<'a, E, F: FloatBounds> Predict<ArrayBase<ViewRepr<&'a F>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<usize>, Dim<[usize; 1]>>> for FixedThresholdClassifier<E>

where E: PredictProba<ArrayView2<'a, F>, Array2<F>>,

fn predict(&self, x: &ArrayView2<'a, F>) -> Result<Array1<usize>>

Make predictions on the provided data

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

Make predictions with confidence intervals

impl<'a, E, F: FloatBounds> PredictProba<ArrayBase<ViewRepr<&'a F>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<F>, Dim<[usize; 2]>>> for FixedThresholdClassifier<E>

where E: PredictProba<ArrayView2<'a, F>, Array2<F>>,

fn predict_proba(&self, x: &ArrayView2<'a, F>) -> Result<Array2<F>>

Predict class probabilities