Struct RandomForestImputer 

pub struct RandomForestImputer<S = Untrained> { /* private fields */ }

Random Forest Imputer

Uses random forest regression/classification to impute missing values. For each feature with missing values, trains a random forest using other features as predictors.

Parameters

• n_estimators - Number of trees in the forest
• max_depth - Maximum depth of trees
• min_samples_split - Minimum samples required to split a node
• min_samples_leaf - Minimum samples required at a leaf node
• max_features - Number of features to consider for best split
• bootstrap - Whether to use bootstrap sampling
• random_state - Random state for reproducibility
• missing_values - The placeholder for missing values

Examples

use sklears_impute::RandomForestImputer;
use sklears_core::traits::{Transform, Fit};
use scirs2_core::ndarray::array;

let X = array![[1.0, 2.0, 3.0], [f64::NAN, 3.0, 4.0], [7.0, f64::NAN, 6.0]];

let imputer = RandomForestImputer::new()
    .n_estimators(100)
    .max_depth(10);
let fitted = imputer.fit(&X.view(), &()).unwrap();
let X_imputed = fitted.transform(&X.view()).unwrap();

Implementations

impl RandomForestImputer<Untrained>

pub fn new() -> Self

Create a new RandomForestImputer instance

pub fn n_estimators(self, n_estimators: usize) -> Self

Set the number of estimators

pub fn max_depth(self, max_depth: usize) -> Self

Set the maximum depth

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

Set the minimum samples split

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

Set the minimum samples leaf

pub fn max_features(self, max_features: String) -> Self

Set the max features strategy

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

Set whether to use bootstrap

pub fn random_state(self, random_state: u64) -> Self

Set the random state

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

Set the missing values placeholder

Trait Implementations

impl<S: Clone> Clone for RandomForestImputer<S>

fn clone(&self) -> RandomForestImputer<S>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for RandomForestImputer<S>

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

Formats the value using the given formatter.

impl Default for RandomForestImputer<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for RandomForestImputer<Untrained>

type Config = ()

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<ViewRepr<&f64>, Dim<[usize; 2]>>, ()> for RandomForestImputer<Untrained>

type Fitted = RandomForestImputer<RandomForestImputerTrained>

The fitted model type

fn fit(self, X: &ArrayView2<'_, Float>, _y: &()) -> SklResult<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 Transform<ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>> for RandomForestImputer<RandomForestImputerTrained>

fn transform(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array2<Float>>

Transform the input data