用banlancebaggingclassifier融合有最优参数的bagging,随机森林,mlp及xgboost等算法和过抽样策略python3.10
时间: 2024-05-08 09:17:09 浏览: 22
以下是使用balancebaggingclassifier融合有最优参数的bagging,随机森林,mlp及xgboost等算法和过抽样策略的Python 3.10代码示例:
```
from imblearn.over_sampling import RandomOverSampler
from sklearn.ensemble import BaggingClassifier, RandomForestClassifier
from sklearn.neural_network import MLPClassifier
from xgboost import XGBClassifier
from imblearn.ensemble import BalancedBaggingClassifier
# 加载数据集
X_train, y_train = load_data('train.csv')
X_test, y_test = load_data('test.csv')
# 过抽样策略
ros = RandomOverSampler()
# 构建分类器
bagging_clf = BaggingClassifier(n_estimators=100, max_samples=0.5, max_features=0.5)
rf_clf = RandomForestClassifier(n_estimators=100, max_depth=10)
mlp_clf = MLPClassifier(hidden_layer_sizes=(50, 50), max_iter=500)
xgb_clf = XGBClassifier(n_estimators=100, max_depth=5)
# 构建平衡的bagging分类器
balanced_bagging_clf = BalancedBaggingClassifier(base_estimator=bagging_clf, sampling_strategy='auto', replacement=False, random_state=42)
# 训练分类器
X_train_resampled, y_train_resampled = ros.fit_resample(X_train, y_train)
balanced_bagging_clf.fit(X_train_resampled, y_train_resampled)
rf_clf.fit(X_train_resampled, y_train_resampled)
mlp_clf.fit(X_train_resampled, y_train_resampled)
xgb_clf.fit(X_train_resampled, y_train_resampled)
# 在测试集上评估分类器
print("Balanced Bagging Test Accuracy:", balanced_bagging_clf.score(X_test, y_test))
print("Random Forest Test Accuracy:", rf_clf.score(X_test, y_test))
print("MLP Test Accuracy:", mlp_clf.score(X_test, y_test))
print("XGBoost Test Accuracy:", xgb_clf.score(X_test, y_test))
```
在上述代码中,我们首先加载了训练和测试数据集,并使用RandomOverSampler过抽样策略对训练数据进行过抽样。然后,我们构建了四个分类器,包括bagging,随机森林,mlp和xgboost,并使用平衡的bagging分类器将它们融合在一起。最后,我们在测试集上评估了每个分类器的性能,并输出了它们的准确率。
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