梯度boosting算法代码实现
时间: 2023-05-24 20:03:00 浏览: 97
A:梯度boosting算法是一种常用的集成学习算法,以下是Python代码实现:
```
import numpy as np
from sklearn.tree import DecisionTreeRegressor
class GradientBoostingRegressor:
def __init__(self, n_estimators=100, learning_rate=0.1, max_depth=3):
self.n_estimators = n_estimators
self.learning_rate = learning_rate
self.max_depth = max_depth
self.trees = []
def fit(self, X, y):
F = np.zeros(len(y))
for i in range(self.n_estimators):
r = y - F
tree = DecisionTreeRegressor(max_depth=self.max_depth)
tree.fit(X, r)
self.trees.append(tree)
F += self.learning_rate * tree.predict(X)
def predict(self, X):
F = np.zeros(len(X))
for tree in self.trees:
F += self.learning_rate * tree.predict(X)
return F
```
在fit方法中,我们首先初始化F为所有样本的平均值,然后迭代训练n_estimators个决策树,每次训练都求出残差r,然后用决策树拟合残差。在拟合完成之后,我们将预测结果乘以学习率learning_rate,然后加到F上。最后在predict方法中,我们将所有决策树的预测结果加权累加,得到最终的输出。