# -*- coding: utf-8 -*- """ Created on Tue May 8 16:19:00 2018 @author: eagle """ from sklearn.datasets import load_digits from sklearn.cross_validation import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.svm import LinearSVC from sklearn.metrics import classification_report #加载数据(1797条,8*8像素的图片) digits = load_digits() print(digits.data.shape) #测试集和训练集分割 X_train,X_test,y_train,y_test = train_test_split(digits.data,digits.target,test_size=0.25,random_state=33) #y_train.shape 1347 #y_test.shape 450 #对数据进行标准化处理 ss = StandardScaler() X_train = ss.fit_transform(X_train) X_test = ss.transform(X_test) #基于线性假设的支持向量机:初始化、训练、预测 lsvc = LinearSVC() lsvc.fit(X_train,y_train) y_predict = lsvc.predict(X_test) #评价 print('The Accuracy of Linear SVC is:',lsvc.score(X_test,y_test)) print(classification_report(y_test,y_predict,target_names=digits.target_names.astype(str)))
学习笔记:支持向量机预测数字
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转载自blog.csdn.net/horserunningnostop/article/details/80264724
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