Struct VotingRegressor 

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

Voting ensemble regressor.

Trains multiple decision tree regressors with different hyperparameter configurations on the full dataset. Final predictions are the average 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).

Examples

use ferrolearn_tree::VotingRegressor;
use ferrolearn_core::{Fit, Predict};
use ndarray::{array, Array1, Array2};

let x = Array2::from_shape_vec((6, 2), vec![
    1.0, 2.0,  2.0, 3.0,  3.0, 3.0,
    5.0, 6.0,  6.0, 7.0,  7.0, 8.0,
]).unwrap();
let y = array![1.0, 2.0, 3.0, 5.0, 6.0, 7.0];

let model = VotingRegressor::<f64>::new()
    .with_max_depths(vec![Some(2), Some(4), None]);
let fitted = model.fit(&x, &y).unwrap();
let preds = fitted.predict(&x).unwrap();
assert_eq!(preds.len(), 6);

Fields

max_depths: Vec<Option<usize>>

Maximum depth settings for each tree in the ensemble.

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.

Implementations

impl<F: Float> VotingRegressor<F>

pub fn new() -> Self

Create a new VotingRegressor with default settings.

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

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

Set the maximum depth settings for each ensemble member.

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.

Trait Implementations

impl<F: Clone> Clone for VotingRegressor<F>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<F: Debug> Debug for VotingRegressor<F>

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

Formats the value using the given formatter.

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

fn default() -> Self

Returns the “default value” for a type.

impl<'de, F> Deserialize<'de> for VotingRegressor<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<F>, Dim<[usize; 1]>>> for VotingRegressor<F>

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

Fit the voting regressor 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 = FittedVotingRegressor<F>

The fitted model type returned by fit.

type Error = FerroError

The error type returned by fit.

impl<F: Float + Send + Sync + 'static> PipelineEstimator<F> for VotingRegressor<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 VotingRegressor<F>

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

where __S: Serializer,

Serialize this value into the given Serde serializer.