Table of Contents

Mojo struct

RandomForest

@memory_only
struct RandomForest[criterion: String = "gini"]

A random forest supporting both classification and regression.

Aliases

  • MODEL_ID = 10
  • criterion_ids = List(String("mse"), String("entropy"), String("gini"), __list_literal__=NoneType(None))

Parameters

  • criterion (String): The function to measure the quality of a split: For classification -> 'entropy', 'gini'; For regression -> 'mse'.

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.
  • trees (UnsafePointer[DecisionTree[criterion], MutAnyOrigin])

Implemented traits

AnyType, CV, Copyable, ImplicitlyDestructible, Movable

Methods

__init__

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

Args:

  • n_trees (Int)
  • 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[String, String])
  • 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:

save

fn save(self, path: String)

Save model data necessary for prediction to the specified path.

Args:

  • self (Self)
  • path (String)

Raises:

load

@staticmethod
fn load[type: UInt8](path: String) -> RandomForest[(load_from_mem RandomForest[criterion].criterion_ids[type])]

Load a saved model from the specified path for prediction.

Parameters:

  • type (UInt8)

Args:

  • path (String)

Returns:

RandomForest[(load_from_mem RandomForest[criterion].criterion_ids[type])]

Raises:

Edit this page