Struct VotingClassifier 

pub struct VotingClassifier<F> {
    pub max_depths: Vec<Option<usize>>,
    pub min_samples_split: usize,
    pub min_samples_leaf: usize,
    pub criterion: ClassificationCriterion,
    /* private fields */
}

Voting ensemble classifier.

Trains multiple decision tree classifiers with different hyperparameter configurations on the full dataset. Final predictions are made by majority vote across all trees.

Diversity is introduced by varying max_depth across the ensemble members. If no explicit depths are provided, a default set of depths is used.

Type Parameters

• F: The floating-point type (f32 or f64).

Fields

max_depths: Vec<Option<usize>>

Maximum depth settings for each tree in the ensemble. Each entry produces one decision tree.

min_samples_split: usize

Minimum number of samples required to split an internal node.

min_samples_leaf: usize

Minimum number of samples required in a leaf node.

criterion: ClassificationCriterion

Splitting criterion for all trees.

Implementations

impl<F: Float> VotingClassifier<F>

pub fn new() -> Self

Create a new VotingClassifier with default settings.

Defaults: max_depths = [Some(2), Some(4), Some(6), None], min_samples_split = 2, min_samples_leaf = 1, criterion = Gini.

pub fn with_max_depths(self, max_depths: Vec<Option<usize>>) -> Self

Set the maximum depth settings for each ensemble member.

Each entry in the vector produces one decision tree.

pub fn with_min_samples_split(self, min_samples_split: usize) -> Self

Set the minimum number of samples required to split a node.

pub fn with_min_samples_leaf(self, min_samples_leaf: usize) -> Self

Set the minimum number of samples required in a leaf node.

pub fn with_criterion(self, criterion: ClassificationCriterion) -> Self

Set the splitting criterion for all trees.

Trait Implementations

impl<F: Clone> Clone for VotingClassifier<F>

fn clone(&self) -> VotingClassifier<F>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<F: Debug> Debug for VotingClassifier<F>

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

Formats the value using the given formatter.

impl<F: Float> Default for VotingClassifier<F>

fn default() -> Self

Returns the “default value” for a type.

impl<'de, F> Deserialize<'de> for VotingClassifier<F>

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

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl<F: Float + Send + Sync + 'static> Fit<ArrayBase<OwnedRepr<F>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<usize>, Dim<[usize; 1]>>> for VotingClassifier<F>

fn fit( &self, x: &Array2<F>, y: &Array1<usize>, ) -> Result<FittedVotingClassifier<F>, FerroError>

Fit the voting classifier by training each decision tree on the full dataset.

Errors

Returns FerroError::ShapeMismatch if x and y have different numbers of samples. Returns FerroError::InsufficientSamples if there are no samples. Returns FerroError::InvalidParameter if configuration is invalid.

type Fitted = FittedVotingClassifier<F>

The fitted model type returned by fit.

type Error = FerroError

The error type returned by fit.

impl<F: Float + ToPrimitive + FromPrimitive + Send + Sync + 'static> PipelineEstimator<F> for VotingClassifier<F>

fn fit_pipeline( &self, x: &Array2<F>, y: &Array1<F>, ) -> Result<Box<dyn FittedPipelineEstimator<F>>, FerroError>

Fit this estimator on the given data.

impl<F> Serialize for VotingClassifier<F>

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

where __S: Serializer,

Serialize this value into the given Serde serializer.