參考：http://scikit-learn.org/stable/modules/model_persistence.html
训练了模型之后，我们希望能够保存下来，遇到新样本时直接使用已经训练好的保存了的模型。而不用又一次再训练模型。
本节介绍pickle在保存模型方面的应用。
（After training a scikit-learn model, it is desirable to have a way to persist the model for future use without having to retrain. The following section gives you an example of how to persist a model with pickle. We’ll also review a few security and maintainability issues when working with pickle serialization.）
1、persistence example
It is possible to save a model in the scikit by using Python’s built-in persistence model, namely :
>>> 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, degree=3, gamma=0.0, kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False)>>> import pickle>>> s = pickle.dumps(clf)>>> clf2 = pickle.loads(s)>>> clf2.predict(X[0])array([0])>>> y[0]0
有些情况下（more efficient on objects that carry large numpy arrays internally）使用joblib’s 取代pickle (joblib.dump & joblib.load)。之后我们甚至能够在还有一个pathon程序中load保存好的模型（pickle也能够。。。）：
>>> from sklearn.externals import joblib>>> joblib.dump(clf, 'filename.pkl') >>> clf = joblib.load('filename.pkl') Note
joblib.dump returns a list of filenames. Each individual numpy array contained in the clf object is serialized as a separate file on the filesystem. All files are required in the same folder when reloading the model with joblib.load.
2、security & maintainability limitations
pickle (and joblib by extension)在maintainability and security方面有些问题。由于：
- Never unpickle untrusted data
- Models saved in one version of scikit-learn might not load in another version.
- The training data, e.g. a reference to a immutable snapshot
- The python source code used to generate the model
- The versions of scikit-learn and its dependencies
- The cross validation score obtained on the training data