Mojo struct

GBDT

@memory_only
struct GBDT

Gradient Boosting with support for both classification and regression.

Aliases

• MODEL_ID = 11
• criterion_ids = List(String("log"), String("softmax"), String("mse"), __list_literal__=NoneType(None))

Fields

• criterion (String): The method to measure the quality of a split: For binary classification -> 'log'; For multi-class classification -> 'softmax'; For regression -> 'mse'.
• loss_g (def(Matrix, Matrix) raises thin -> Matrix)
• loss_h (def(Matrix) raises thin -> Matrix)
• n_trees (Int): The number of boosting stages to perform.
• min_samples_split (Int): The minimum number of samples required to split an internal node.
• max_depth (Int): The maximum depth of the tree.
• learning_rate (Float32): Learning rate.
• reg_lambda (Float32): The L2 regularization parameter.
• reg_alpha (Float32): The L1 regularization parameter.
• gamma (Float32): Minimum loss reduction required to make a further partition on a leaf node of the tree.
• n_bins (Int): Generates histogram boundaries as possible threshold values when n_bins >= 2 instead of all possible values.
• trees (Pointer[BDecisionTree, MutUntrackedOrigin])
• score_start (Float32)
• num_class (Int)

Implemented traits

AnyType, CV, Copyable, Deinitable, Movable

Methods

__init__

fn def __init__(out self, criterion: String = "log", n_trees: Int = Int(10), min_samples_split: Int = Int(10), max_depth: Int = Int(3), learning_rate: Float32 = 0.10000000000000001, reg_lambda: Float32 = 1, reg_alpha: Float32 = 0, gamma: Float32 = 0, n_bins: Int = Int(0))

Args:

• criterion (String)
• n_trees (Int)
• min_samples_split (Int)
• max_depth (Int)
• learning_rate (Float32)
• reg_lambda (Float32)
• reg_alpha (Float32)
• gamma (Float32)
• n_bins (Int)
• self (Self)

Returns:

Self

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

Args:

• params (Dict[String, String])
• self (Self)

Returns:

Self

Raises:

__deinit__

fn def __deinit__(deinit self)

Args:

• self (Self)

fit

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

Fit the gradient boosting model.

Args:

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

Raises:

predict

fn 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

fn def save(self, path: String)

Save model data necessary for prediction to the specified path.

Args:

• self (Self)
• path (String)

Raises:

load

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

Load a saved model from the specified path for prediction.

Args:

• path (String)

Returns:

Self

Raises: