本文主要是介绍分类学习-支持向量机（Scikit-learn），希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
手写体数字识别
1、手写体数据读取
from sklearn.datasets import load_digits
digits = load_digits() #获得的手写体数据图片存储在digits变量中
print(digits.data.shape)2、数据分割
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.25, random_state=33)
print(y_train.shape)
print(y_test.shape)#用支持向量机进行分类处理
from sklearn.preprocessing import StandardScaler
from sklearn.svm import LinearSVC#标准化数据
ss = StandardScaler()
X_train = ss.fit_transform(X_train)
X_test = ss.transform(X_test)#训练模型
lsvc = LinearSVC()
lsvc.fit(X_train, y_train)
y_predict = lsvc.predict(X_test)#性能分析
from sklearn.metrics import classification_report
print('Accuracy of Linear SVC',lsvc.score(X_test, y_test))
print(classification_report(y_test, y_predict, target_names = digits.target_names.astype(str)))这篇关于分类学习-支持向量机（Scikit-learn）的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
