Talk is cheap , show U the code.
该源码注释比较全面，需要对SKlearn有一定的了解，
当然，你也可以把它视作黑箱，做个调包侠也是大侠。
方法一（pickle）：
>>> 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)
# 保存训练结果
>>> import pickle
>>> s = pickle.dumps(clf)
# 调用训练结果，并进行测试
>>> clf2 = pickle.loads(s)
>>> clf2.predict(X[0:1])
array([0])
>>> y[0]方法二（joblib）：
from sklearn.externals import joblib
>>> from sklearn import svm
>>> X = [[0, 0], [1, 1]]
>>> y = [0, 1]
>>> clf = svm.SVC()
>>> clf.fit(X, y)
>>> clf.fit(train_X,train_y)
# 保存训练结果
>>> joblib.dump(clf, "train_model.m")
# 调用训练结果，并进行测试
>>> clf = joblib.load("train_model.m")
clf.predit(test_X)