## How to save trained model in Python
## DataSet: skleran.datasets.load_breast_cancer()
def Snippet_185():
print()
print(format('How to save trained model in Python','*^82'))
import warnings
warnings.filterwarnings("ignore")
# load libraries
from sklearn import model_selection, datasets
from sklearn.tree import DecisionTreeClassifier
from sklearn.externals import joblib
import pickle
# load dataset
dataset = datasets.load_breast_cancer()
X = dataset.data; y = dataset.target
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.25)
# Fit the model on 33%
model = DecisionTreeClassifier()
model.fit(X_train, y_train)
# save the model to disk using Pickle
filename = 'trained_model.pickle'
pickle.dump(model, open(filename, 'wb'))
# load the model from disk
loaded_model = pickle.load(open(filename, 'rb'))
result = loaded_model.score(X_test, y_test)
print(); print(result)
# save the model to disk using Joblib
filename = 'trained_model.joblib'
joblib.dump(model, filename)
# load the model from disk
loaded_model = joblib.load(filename)
result = loaded_model.score(X_test, y_test)
print(); print(result)
Snippet_185()