A Julia implementation of Gradient Boosting — an ensemble learning method that builds a strong predictive model by sequentially fitting decision trees to the residuals of the previous iteration.
This package is an official entry in the Julia General Registry and can be installed via Pkg.jl.
Pkg.add("GradientBoosting")using GradientBoosting
# Train the model
predictions, models, history = GradientBoosting.fit(y_train, X_train, lr, max_depth, n_trees)
# Predict on new data
test_predictions = GradientBoosting.predict(y_test, X_test, lr, models)| Parameter | Description |
|---|---|
y |
Target variable (vector) |
X |
Feature matrix |
lr |
Learning rate — controls the contribution of each tree |
max_depth |
Maximum depth of each decision tree |
n_trees |
Number of boosting iterations (trees) |