adaboost算法代码实现

AdaBoost（自适应增强）算法的实现非常简单，并且可以用来解决各种分类问题。下面是一个简单的AdaBoost算法实现的例子：# 从数据集中获取训练样本
train_set = get_train_set()# 设置初始权重
weights = np.array([1.0/train_set.shape[0] for _ in range(train_set.shape[0])])# 训练基本分类器
classifier_list = []
for _ in range(num_classifier):
classifier = train_classifier(train_set, weights)
classifier_list.append(classifier)# 计算每个分类器的错误率
error_rates = []
for classifier in classifier_list:
error_rate = compute_error_rate(classifier, train_set, weights)
error_rates.append(error_rate)# 计算分类器的权重
weights_list = []
for error_rate in error_rates:
weight = 0.5 * np.log((1 - error_rate) / error_rate)
weights_list.append(weight)# 计算最终的分类结果
final_classifier = construct_final_classifier(classifier_list, weights_list)