In [3]:

    

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)


    

    
Out[3]:





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)

In [4]:

    

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


    

    
Out[4]:





0

In [ ]: