How to save a trained model by scikit-learn? [duplicate]

You can use:

1. pickle

from sklearn import svm
from sklearn import datasets

iris = datasets.load_iris()
X, y = iris.data, iris.target

clf = svm.SVC()
clf.fit(X, y)  

##########################
# SAVE-LOAD using pickle #
##########################
import pickle

# save
with open('model.pkl','wb') as f:
    pickle.dump(clf,f)

# load
with open('model.pkl', 'rb') as f:
    clf2 = pickle.load(f)

clf2.predict(X[0:1])

2. joblib

From scikit-learn documentation:

In the specific case of scikit-learn, it may be better to use joblib’s
replacement of pickle (dump & load), which is more efficient on
objects that carry large numpy arrays internally as is often the case
for fitted scikit-learn estimators, but can only pickle to the disk
and not to a string:

from sklearn import svm
from sklearn import datasets

iris = datasets.load_iris()
X, y = iris.data, iris.target

clf = svm.SVC()
clf.fit(X, y)  

##########################
# SAVE-LOAD using joblib #
##########################
import joblib

# save
joblib.dump(clf, "model.pkl") 

# load
clf2 = joblib.load("model.pkl")

clf2.predict(X[0:1])