Mojo struct

DecisionTree

@memory_only
struct DecisionTree

A decision tree supporting both classification and regression.

Aliases

• __del__is_trivial = False
• __moveinit__is_trivial = False
• __copyinit__is_trivial = False

Fields

• criterion (String): The function to measure the quality of a split: For classification -> 'entropy', 'gini'; For regression -> 'mse'.
• loss_func (fn(Matrix, Matrix, Float32) raises -> Float32)
• c_func (fn(Float32, List[Int]) raises -> Float32)
• r_func (fn(Float32, Float32, Float32) raises -> Float32)
• min_samples_split (Int): The minimum number of samples required to split an internal node.
• max_depth (Int): The maximum depth of the tree.
• n_feats (Int): The number of features to consider when looking for the best split.
• root (UnsafePointer[Node, MutAnyOrigin])

Implemented traits

AnyType, CV, Copyable, ImplicitlyCopyable, ImplicitlyDestructible, Movable

Methods

__init__

fn __init__(out self, criterion: String = "gini", min_samples_split: Int = 2, max_depth: Int = 100, n_feats: Int = -1, random_state: Int = 42)

Args:

• criterion (String)
• min_samples_split (Int)
• max_depth (Int)
• n_feats (Int)
• random_state (Int)
• self (Self)

Returns:

Self

fn __init__(out self, params: Dict[String, String])

Args:

• params (Dict)
• self (Self)

Returns:

Self

Raises:

__del__

fn __del__(deinit self)

Args:

• self (Self)

fit

fn fit(mut self, X: Matrix, y: Matrix)

Build a decision tree from the training set.

Args:

• self (Self)
• X (Matrix)
• y (Matrix)

Raises:

fit_weighted

fn fit_weighted(mut self, X: Matrix, y_with_weights: Matrix)

Build a decision tree from a weighted training set.

Args:

• self (Self)
• X (Matrix)
• y_with_weights (Matrix)

Raises:

predict

fn predict(self, X: Matrix) -> Matrix

Predict class or regression value for X.

Args:

• self (Self)
• X (Matrix)

Returns:

Matrix: The predicted values.

Raises: