CustomGradientBoostingRegressor

custom_gbrt.py

class CustomGradientBoostingRegressor:
    
    def __init__(self, learning_rate, n_estimators, max_depth=1):
        self.learning_rate = learning_rate
        self.n_estimators = n_estimators
        self.max_depth = max_depth
        self.trees = []
        
    def fit(self, X, y):
        
        self.F0 = y.mean()
        Fm = self.F0
        
        for _ in range(self.n_estimators):
            r = y - Fm
            tree = DecisionTreeRegressor(max_depth=self.max_depth, random_state=0)
            tree.fit(X, r)
            gamma = tree.predict(X)
            Fm += self.learning_rate * gamma
            self.trees.append(tree)
            
    def predict(self, X):
        
        Fm = self.F0
        
        for i in range(self.n_estimators):
            Fm += self.learning_rate * self.trees[i].predict(X)
            
        return Fm