Table of Contents

Mojo struct

DecisionTree

@memory_only
struct DecisionTree

A decision tree supporting both classification and regression.

Aliases

  • MODEL_ID = 9

Fields

  • criterion (String): The function to measure the quality of a split: For classification -> 'entropy', 'gini'; For regression -> 'mse'.
  • loss_func (def(Matrix, Matrix, Float32) raises -> Float32)
  • c_func (def(Float32, List[Int]) raises -> Float32)
  • r_func (def(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__

def __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

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

Args:

  • params (Dict)
  • self (Self)

Returns:

Self

Raises:

__del__

def __del__(deinit self)

Args:

  • self (Self)

fit

def 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

def 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

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

Predict class or regression value for X.

Args:

  • self (Self)
  • X (Matrix)

Returns:

Matrix: The predicted values.

Raises:

save

def save(self, path: String)

Save model data necessary for prediction to the specified path.

Args:

  • self (Self)
  • path (String)

Raises:

load

@staticmethod
def load(path: String) -> Self

Load a saved model from the specified path for prediction.

Args:

  • path (String)

Returns:

Self

Raises:

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