将一个模型的输出与另一个模型相结合

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# 加载数据
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 定义基础模型
base_models = [
    ('rf', RandomForestClassifier(n_estimators=100)),
    ('svc', SVC(probability=True))
]

# 训练基础模型并获取预测概率
meta_features_train = []
for name, model in base_models:
    model.fit(X_train, y_train)
    meta_features_train.append(model.predict_proba(X_train))

# 训练元模型
meta_model = LogisticRegression()
meta_model.fit(np.hstack(meta_features_train), y_train)

# 在测试集上进行预测
meta_features_test = []
for name, model in base_models:
    meta_features_test.append(model.predict_proba(X_test))

y_pred = meta_model.predict(np.hstack(meta_features_test))
print("Accuracy:", accuracy_score(y_test, y_pred))