Struct DecisionTreeValidParams

pub struct DecisionTreeValidParams<F, L> { /* private fields */ }

The set of hyperparameters that can be specified for fitting a decision tree.

Example

use linfa_trees::{DecisionTree, SplitQuality};
use linfa_datasets::iris;
use linfa::prelude::*;

// Initialize the default set of parameters
let params = DecisionTree::params();
// Set the parameters to the desired values
let params = params.split_quality(SplitQuality::Entropy).max_depth(Some(5)).min_weight_leaf(2.);

// Load the data
let (train, val) = linfa_datasets::iris().split_with_ratio(0.9);
// Fit the decision tree on the training data
let tree = params.fit(&train).unwrap();
// Predict on validation and check accuracy
let val_accuracy = tree.predict(&val).confusion_matrix(&val).unwrap().accuracy();
assert!(val_accuracy > 0.9);

Implementations

impl<F: Float, L> DecisionTreeValidParams<F, L>

pub fn split_quality(&self) -> SplitQuality

pub fn max_depth(&self) -> Option<usize>

pub fn min_weight_split(&self) -> f32

pub fn min_weight_leaf(&self) -> f32

pub fn min_impurity_decrease(&self) -> F

Trait Implementations

impl<F: Clone, L: Clone> Clone for DecisionTreeValidParams<F, L>

fn clone(&self) -> DecisionTreeValidParams<F, L>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<F: Debug, L: Debug> Debug for DecisionTreeValidParams<F, L>

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

Formats the value using the given formatter.

impl<F: Float, L: Label + Debug, D, T> Fit<ArrayBase<D, Dim<[usize; 2]>>, T, Error> for DecisionTreeValidParams<F, L>

where D: Data<Elem = F>, T: AsSingleTargets<Elem = L> + Labels<Elem = L>,

fn fit( &self, dataset: &DatasetBase<ArrayBase<D, Ix2>, T>, ) -> Result<Self::Object>

Fit a decision tree using hyperparamters on the dataset consisting of a matrix of features x and an array of labels y.

type Object = DecisionTree<F, L>

impl<F: PartialEq, L: PartialEq> PartialEq for DecisionTreeValidParams<F, L>

fn eq(&self, other: &DecisionTreeValidParams<F, L>) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl<F: Copy, L: Copy> Copy for DecisionTreeValidParams<F, L>

impl<F, L> StructuralPartialEq for DecisionTreeValidParams<F, L>