机器学习集成模型学习——投票集成Voting（二）_基于投票机制的集成模型

Voting集成

投票机制就是多个模型分别预测，然后投票，票数最高的就是整个模型最后的效果

案例代码

from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier, VotingClassifier
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# ============= 准备数据 =============
x, y = make_classification(n_samples=10000, n_classes=4, n_informative=5)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=100)

# ============= 准备模型 =============
clf1 = LogisticRegression(multi_class='multinomial', random_state=1)
clf2 = RandomForestClassifier(n_estimators=50, random_state=1)
clf3 = GaussianNB()

# ============= 集成模型 =============
vot_classifier = VotingClassifier(estimators=[
    ('lr', clf1),
    ('rf', clf2),
    ('gnb', clf3)],
    voting='soft',
    flatten_transform=True)

# ============= 开始预测 =============
vot_classifier.fit(x_train, y_train)
print("acc：", accuracy_score(vot_classifier.predict(x_test), y_test))
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27

参考文章

sklearn voting 官方文档：https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.VotingClassifier.html