小贝叶斯和SVM模型

最新推荐文章于 2026-01-03 21:39:19 发布

原创最新推荐文章于 2026-01-03 21:39:19 发布 · 391 阅读

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#支持向量机 #机器学习 #算法

文章展示了使用Python的Scikit-Learn库实现SVM和朴素贝叶斯分类器对Iris数据集进行训练和测试的过程，比较了两者在训练集和测试集上的准确率，并提供了详细的分类报告。

from sklearn import datasets
from sklearn import neighbors
from sklearn import svm
from sklearn import naive_bayes
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
svm=svm.SVC()
bayes=naive_bayes.GaussianNB()
iris=datasets.load_iris()
X=iris.data
Y=iris.target
x_train,x_test,y_train,y_test=train_test_split(X,Y,test_size=0.3,random_state=0)
svm.fit(x_train,y_train)
print("SVM模型训练集的准确率：%.3f"%svm.score(x_train,y_train))
print("SVM模型测试集的准确率：%.3f"%svm.score(x_test,y_test))
bayes.fit(x_train,y_train)
y_hat=svm.predict(x_test)
y_hat=bayes.predict(x_test)
print(classification_report(y_test,y_hat,target_names=target_name))
print("贝叶斯模型训练集的准确率：%.3f"%bayes.score(x_train,y_train))
print("贝叶斯模型测试集的准确率：%.3f"%bayes.score(x_test,y_test))
target_name=['setosa','versicolor','virginica']
y_hat=svm.predict(x_test)
y_hat=bayes.predict(x_test)
print(classification_report(y_test,y_hat,target_names=target_name))

SVM模型训练集的准确率：0.971

SVM模型测试集的准确率：0.978

precision recall f1-score support setosa 1.00 1.00 1.00 16 versicolor 1.00 1.00 1.00 18 virginica 1.00 1.00 1.00 11

accuracy 1.00 45 macro avg 1.00 1.00 1.00 45 weighted avg 1.00 1.00 1.00 45

贝叶斯模型训练集的准确率：0.943

贝叶斯模型测试集的准确率：1.000

precision recall f1-score support setosa 1.00 1.00 1.00 16 versicolor 1.00 1.00 1.00 18 virginica 1.00 1.00 1.00 11

accuracy 1.00 45 macro avg 1.00 1.00 1.00 45 weighted avg 1.00 1.00 1.00 45