stackingclassifier代码实现
时间: 2023-06-27 11:04:32 浏览: 40
StackingClassifier是一种集成学习的方法,它将多个不同的分类器的输出作为输入,再使用元分类器来进行最终的分类预测。下面是一个简单的StackingClassifier的代码实现:
```python
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.datasets import make_classification
from sklearn.base import clone, BaseEstimator, TransformerMixin
class StackingClassifier(BaseEstimator, TransformerMixin):
def __init__(self, base_classifiers, meta_classifier):
self.base_classifiers = base_classifiers
self.meta_classifier = meta_classifier
def fit(self, X, y):
self.base_classifiers_ = [clone(clf) for clf in self.base_classifiers]
self.meta_classifier_ = clone(self.meta_classifier)
# 训练基分类器
for clf in self.base_classifiers_:
clf.fit(X, y)
# 生成基分类器的输出
base_predictions = np.column_stack([clf.predict(X) for clf in self.base_classifiers_])
# 训练元分类器
self.meta_classifier_.fit(base_predictions, y)
return self
def predict(self, X):
base_predictions = np.column_stack([clf.predict(X) for clf in self.base_classifiers_])
return self.meta_classifier_.predict(base_predictions)
# 创建一个分类数据集
X, y = make_classification(n_samples=1000, n_features=20, n_informative=10, n_classes=2,
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)
# 定义基分类器
base_classifiers = [RandomForestClassifier(n_estimators=100, random_state=42),
GradientBoostingClassifier(n_estimators=100, random_state=42)]
# 定义元分类器
meta_classifier = LogisticRegression(random_state=42)
# 定义StackingClassifier
stacking_clf = StackingClassifier(base_classifiers=base_classifiers, meta_classifier=meta_classifier)
# 训练StackingClassifier
stacking_clf.fit(X_train, y_train)
# 预测测试集
y_pred = stacking_clf.predict(X_test)
# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
```
在这个例子中,我们使用了两个基分类器(随机森林和梯度提升树)和一个元分类器(逻辑回归)。首先,我们训练基分类器,并使用它们的输出来训练元分类器。然后,我们使用StackingClassifier来对测试集进行预测,并计算准确率。