我在做回归任务，发现网上关于Tradaboost的实例不是很多，想请教一下，可否看看Tradaboost可否运用于回归，Tradaboost实例是什么？
迁移学习Tradaboost在Python上的实现，一般需要使用机器学习库如Scikit-learn或TensorFlow等。以下是一个简单的示例代码：
from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# 生成数据集
X, y = make_classification(n_samples=1000, n_features=10, n_informative=5, n_redundant=0, random_state=42)
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 构建基分类器
base_clf = DecisionTreeClassifier(max_depth=2)
# 构建传统的AdaBoost模型
ada_clf = AdaBoostClassifier(base_estimator=base_clf, n_estimators=50, random_state=42)
ada_clf.fit(X_train, y_train)
y_pred = ada_clf.predict(X_test)
print("Accuracy score of traditional AdaBoost:", accuracy_score(y_test, y_pred))
# 构建TransferBoost模型
transfer_clf = AdaBoostClassifier(base_estimator=base_clf, n_estimators=50, random_state=42)
transfer_clf.fit(X_train, y_train, sample_weight=get_sample_weights(X_train, y_train, X_test, y_test))
y_pred = transfer_clf.predict(X_test)
print("Accuracy score of TransferBoost:", accuracy_score(y_test, y_pred))
其中，get_sample_weights是一个用于计算样本权重的函数，可以根据不同的迁移学习方法进行修改。