SVM对sklearn自带手写数字数据集进行分类

sklearn自带一些数据集，其中手写数字数据集可通过load_digits加载，我找到load_digits里头是这样

def load_linnerud():
    """Load and return the linnerud dataset (multivariate regression).

    Samples total: 20
    Dimensionality: 3 for both data and targets
    Features: integer
    Targets: integer

    Returns
    -------
    data : Bunch
        Dictionary-like object, the interesting attributes are: 'data' and
        'targets', the two multivariate datasets, with 'data' corresponding to
        the exercise and 'targets' corresponding to the physiological
        measurements, as well as 'feature_names' and 'target_names'.
    """
    base_dir = join(dirname(__file__), 'data/')
    # Read data
    data_exercise = np.loadtxt(base_dir + 'linnerud_exercise.csv', skiprows=1)
    data_physiological = np.loadtxt(base_dir + 'linnerud_physiological.csv',
                                    skiprows=1)
    # Read header
    with open(base_dir + 'linnerud_exercise.csv') as f:
        header_exercise = f.readline().split()
    with open(base_dir + 'linnerud_physiological.csv') as f:
        header_physiological = f.readline().split()
    with open(dirname(__file__) + '/descr/linnerud.rst') as f:
        descr = f.read()

    return Bunch(data=data_exercise, feature_names=header_exercise,
                 target=data_physiological,
                 target_names=header_physiological,
                 DESCR=descr)

有数据说明，很详细，也可以看到实际那批数据来自一个csv文件，可以按照提示挑一个样例show一下看看

import pylab as pl
digits = load_digits()
# 数据纬度，1797幅图，8*8
# 显示一副图片
# pl.gray()
# pl.matshow(digits.images[0])
# pl.show()

言归正传，调用 sklearn的svm包里的LinearSVC做一下分类实验

# -*- coding:utf-8 -*-
import sys
from sklearn.datasets import load_digits  # 加载手写数字识别数据
import pylab as pl
from sklearn.cross_validation import train_test_split  # 训练测试数据分割
from sklearn.preprocessing import StandardScaler  # 标准化工具
from sklearn.svm import LinearSVC
from sklearn.metrics import classification_report  # 预测结果分析工具

reload(sys)
sys.setdefaultencoding('utf-8')


digits = load_digits()
# 数据纬度，1797幅图，8*8
print digits.data.shape

# 分割数据
X_train, X_test, Y_train, Y_test = train_test_split(digits.data, digits.target, test_size=0.25, random_state=33)

ss = StandardScaler()
# fit是实例方法，必须由实例调用
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 classification_report(Y_test, Y_predict, target_names=digits.target_names.astype(str))

训练集与测试集1比3分割，分类预测的性能如下：

             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

需要记住几个性能指标的含义

precision为精确率，是真阳结果与所有被判为阳性结果的比。

recall为召回率， 是真阳结果与所有实际为阳性数据的比。

真阳者，实际为真且判为真。