adaboost python代码

### 关于AdaBoost算法的Python实现

AdaBoost是一种增强学习方法，通过组合多个弱分类器来创建强分类器。以下是基于决策树桩（Decision Stump）作为基础估计器的一个简单AdaBoost实现例子。

import numpy as np
from sklearn.tree import DecisionTreeClassifier

class AdaBoost:
    def __init__(self, n_estimators=50):
        self.n_estimators = n_estimators
        self.models = []
        self.alphas = []

    def fit(self, X, y):
        n_samples, _ = X.shape
        w = np.full(n_samples, (1 / n_samples))

        for m in range(self.n_estimators):
            model = DecisionTreeClassifier(max_depth=1)
            model.fit(X, y, sample_weight=w)

            pred = model.predict(X)

            error_m = np.sum(w * (pred != y)) / np.sum(w)

            alpha_m = 0.5 * np.log((1 - error_m) / (error_m + 1e-10))
            
            w *= np.exp(-alpha_m * y * pred)
            
            w /= np.sum(w)

            self.models.append(model)
            self.alphas.append(alpha_m)

    def predict(self, X):
        ensemble_preds = [alpha * model.predict(X) for model, alpha in zip(self.models, self.alphas)]
        
        return np.sign(sum(ensemble_preds)).astype(int)

# Example usage:

if __name__ == "__main__":
    from sklearn.datasets import make_classification
    from sklearn.model_selection import train_test_split
    
    X, y = make_classification(
        n_features=2,
        n_redundant=0,
        n_informative=2,
        random_state=1,
        n_clusters_per_class=1
    )
    
    y[y == 0] = -1
    
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y)
    
    clf = AdaBoost()
    clf.fit(X_train, y_train)
    
    predictions = clf.predict(X_test)

此代码定义了一个`AdaBoost`类，该类实现了基本的Adaboost训练逻辑以及预测函数[^1]。对于每一个基分类器，在每一轮迭代过程中计算其权重更新因子α，并据此调整样本分布权值w以便下一次迭代能够更加关注被错误分类的数据点。最终模型预测结果由所有加权后的弱分类器共同决定。