python怎么用easyensamble和随机森林共同预测

使用EasyEnsemble和随机森林共同预测，可以采用以下步骤：

1.导入所需库和数据集

from imblearn.ensemble import EasyEnsembleClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9], 
                            n_informative=3, n_redundant=1, flip_y=0, n_features=20, 
                            n_clusters_per_class=1, n_samples=1000, random_state=10)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=10)

2.使用EasyEnsembleClassifier对训练集进行重采样

eec = EasyEnsembleClassifier(n_estimators=10, random_state=10)
eec.fit(X_train, y_train)

3.使用随机森林对每个重采样数据集进行训练和预测

rfc = RandomForestClassifier(n_estimators=10, random_state=10)
preds = []
for X_resampled, y_resampled in eec:
    rfc.fit(X_resampled, y_resampled)
    y_pred = rfc.predict(X_test)
    preds.append(y_pred)

4.对每个预测结果进行投票，得到最终预测结果

import numpy as np
final_preds = np.round(np.mean(np.array(preds), axis=0))

5.计算模型的性能指标

from sklearn.metrics import accuracy_score, f1_score, recall_score, precision_score
print("Accuracy:",accuracy_score(y_test, final_preds))
print("F1 score:",f1_score(y_test, final_preds))
print("Recall:",recall_score(y_test, final_preds))
print("Precision:",precision_score(y_test, final_preds))

综上，以上步骤可以将EasyEnsembleClassifier和随机森林结合起来，实现分类预测任务。