Mojo struct

RandomForest

@memory_only
struct RandomForest

A random forest supporting both classification and regression.

Aliases

• __del__is_trivial = False

Fields

• n_trees (Int): The number of trees in the forest.
• 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.
• criterion (String): The function to measure the quality of a split: For classification -> 'entropy', 'gini'; For regression -> 'mse'.
• trees (UnsafePointer[DecisionTree, MutAnyOrigin])

Implemented traits

AnyType, CV, ImplicitlyDestructible

Methods

__init__

fn __init__(out self, n_trees: Int = 10, min_samples_split: Int = 2, max_depth: Int = 100, n_feats: Int = -1, criterion: String = "gini", random_state: Int = 42)

Args:

• n_trees (Int)
• min_samples_split (Int)
• max_depth (Int)
• n_feats (Int)
• criterion (String)
• 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 forest of trees from the training set.

Args:

• self (Self)
• X (Matrix)
• y (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: