机器学习---支持向量机(分类) 笔记

1.手写体数据读取代码样例

# 1.load hand write datasets
from sklearn.datasets import load_digits

digits = load_digits()
print digits.data.shape

输出:

(1797, 64)

2.手写体数据分割代码样例

# 2.data split to train and test data
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

输出:

(1347,)
(450,)

3.使用支持向量机(分类)对手写体数字图像进行识别

# 3.to predict
from sklearn.preprocessing import StandardScaler
from sklearn.svm import LinearSVC

ss = StandardScaler()
x_train = ss.fit_transform(x_train)
x_test = ss.transform(x_test)

lsvc = LinearSVC()
lsvc.fit(x_train, y_train)
y_predict = lsvc.predict(x_test)

print 'The Accuracy of LinearSVC is ', lsvc.score(x_test,y_test)

from sklearn.metrics import classification_report
print classification_report(y_test, y_predict, target_names=digits.target_names.astype(str))

输出:

The Accuracy of LinearSVC is  0.953333333333
             precision    recall  f1-score   support

          0       0.92      1.00      0.96        35
          1       0.96      0.98      0.97        54
          2       0.98      1.00      0.99        44
          3       0.93      0.93      0.93        46
          4       0.97      1.00      0.99        35
          5       0.94      0.94      0.94        48
          6       0.96      0.98      0.97        51
          7       0.92      1.00      0.96        35
          8       0.98      0.84      0.91        58
          9       0.95      0.91      0.93        44

avg / total       0.95      0.95      0.95       450