随机森林和Adaboost结合的分类算法代码
时间: 2023-10-05 08:09:22 浏览: 61
这里提供一个Python实现的随机森林和Adaboost结合的分类算法代码:
```python
from sklearn.ensemble import RandomForestClassifier, 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
# 生成随机数据集
X, y = make_classification(n_samples=1000, n_features=10, n_informative=5, n_classes=2, random_state=1)
# 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=1)
# 定义随机森林分类器
rfc = RandomForestClassifier(n_estimators=50, max_depth=5, random_state=1)
# 定义AdaBoost分类器
abc = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=3), n_estimators=50, learning_rate=0.1, random_state=1)
# 定义结合分类器
class CombinedClassifier():
def __init__(self, classifiers):
self.classifiers = classifiers
def fit(self, X, y):
for clf in self.classifiers:
clf.fit(X, y)
def predict(self, X):
predictions = []
for clf in self.classifiers:
predictions.append(clf.predict(X))
# 对预测结果进行投票,得到最终结果
return [max(set(prediction), key=prediction.count) for prediction in zip(*predictions)]
# 定义结合分类器
cc = CombinedClassifier([rfc, abc])
# 训练结合分类器
cc.fit(X_train, y_train)
# 在测试集上进行预测
y_pred = cc.predict(X_test)
# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy: %.2f%%' % (accuracy * 100))
```
在上述代码中,我们使用`sklearn`库中的`RandomForestClassifier`和`AdaBoostClassifier`定义了两个分类器,然后实现了一个结合分类器`CombinedClassifier`,该分类器将多个分类器的预测结果进行投票,得到最终的预测结果。最后,在随机数据集上进行训练和测试,并计算准确率。