GradBoost takes in a regression model with methods .fit(X, y) and .predict(X), a set of testing X data to be predicted, training X and y data to fit on, and returns a tuple of the predicted y-values for the training data and testing data. The boosting rounds and learning rate are adjustable. The flexibility of model choice allows custom loss functions for the weak learners.
Package to perform gradient boosting for regression using sklearn style regression models.
More details on the gradient boosting are here: https://en.wikipedia.org/wiki/Gradient_boosting
Clone this repository, move into the directory, and install with pip:
git clone https://github.com/jkclem/GradBoost.git
cd GradBoost
pip install .
In your Python code you can import it as:
from GradBoost import GradBoost
The example folder in this repository applies this function to simulated data using sklearn's DecisionTreeRegressor and RidgeCV models.