adaboost算法Python实现

好的，下面是adaboost算法Python实现的示例代码：

import numpy as np

class AdaBoost:
    def __init__(self, n_estimators=50, learning_rate=1.0):
        self.n_estimators = n_estimators
        self.learning_rate = learning_rate
        self.estimators = []
        self.alphas = []

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

        for i in range(self.n_estimators):
            clf = DecisionTreeClassifier(max_depth=1)
            clf.fit(X, y, sample_weight=w)
            y_pred = clf.predict(X)
            error = np.sum(w * (y_pred != y))
            alpha = self.learning_rate * (np.log(1 - error) - np.log(error))
            w = w * np.exp(alpha * (y_pred != y))
            w = w / np.sum(w)
            self.estimators.append(clf)
            self.alphas.append(alpha)

    def predict(self, X):
        n_samples = X.shape[0]
        y_pred = np.zeros((n_samples, 1))
        for i in range(self.n_estimators):
            y_pred += self.alphas[i] * self.estimators[i].predict(X).reshape(-1, 1)
        y_pred = np.sign(y_pred)
        return y_pred

在这个实现中，我们使用了决策树作为弱分类器，每次训练一个弱分类器，并根据其错误率和预测结果更新样本权重，最后将所有弱分类器的预测结果加权求和，得到最终的分类结果。