>>> from sklearn import svm
>>> from sklearn import datasets
>>> clf = svm.SVC()
>>> iris = datasets.load_iris()
>>> X, y = iris.data, iris.target
>>> clf.fit(X, y)  
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
    decision_function_shape=None
 
, degree=3, gamma='auto', kernel='rbf',
    max_iter=-1, probability=False, random_state=None, shrinking=True,
    tol=0.001, verbose=False)

方案1

>>> import pickle
>>> s = pickle.dumps(clf)
>>> clf2 = pickle.loads(s)
>>> clf2.predict(X[0:1])
array([0])
>>> 
 
y[0]
0

或者：

#儲存
import cPickle

with open('filename.pkl', 'wb') as f:
    cPickle.dump(forest, f)

#讀取
with open('filename.pkl', 'rb') as f:
    forest = cPickle.load(f)

方案2

>>> from sklearn.externals import joblib
>>> joblib.dump(clf, 'filename.pkl') 

>>> clf = joblib.load('filename.pkl'
 
) 

但是有一個問題需要注意，即32位python和64位python並不能無縫連線使用，即如果用32python儲存，然後用64位python進行讀取大概率會報錯，反之亦然：

報錯如下：

ValueError: Buffer dtype mismatch, expected 'SIZE_t' but got 'int' 

記錄這個是因為工作的時候遇到這個問題，費了不少勁。
如果想檢視自己的python是32位還是64位的，可以這樣：

In [287]: import platform
In [288]: platform.architecture()
Out[288]: ('32bit', 'WindowsPE')