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。