Model_Persistence

from sklearn import datasets
iris = datasets.load_iris()
digits = datasets.load_digits()
print(digits.data)
[[ 0.  0.  5. ...  0.  0.  0.]
 [ 0.  0.  0. ... 10.  0.  0.]
 [ 0.  0.  0. ... 16.  9.  0.]
 ...
 [ 0.  0.  1. ...  6.  0.  0.]
 [ 0.  0.  2. ... 12.  0.  0.]
 [ 0.  0. 10. ... 12.  1.  0.]]

digits.target
array([0, 1, 2, ..., 8, 9, 8])

from sklearn import svm
clf = svm.SVC(gamma=0.001, C=100.)
clf.fit(digits.data[:-1], digits.target[:-1])
SVC(C=100.0, gamma=0.001)

clf.predict(digits.data[-1:])
array([8])

from sklearn import svm
from sklearn import datasets
clf = svm.SVC()
X, y = datasets.load_iris(return_X_y=True)
clf.fit(X, y)
SVC()

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

y[0]
0