PYTHON

model pickle file create

import pickle

# save the model to disk
filename = 'finalized_model.sav'
pickle.dump(model, open(filename, 'wb'))
 
# some time later...
 
# load the model from disk
loaded_model = pickle.load(open(filename, 'rb'))
result = loaded_model.score(X_test, Y_test)
print(result)

save model pickle

with open('model_pkl', 'wb') as files:
    pickle.dump(model, files)
with open('model_pkl' , 'rb') as f:
    lr = pickle.load(f)   

save and load a machine learning model using Pickle

#Save the model using pickle
import pickle
# save the model to disk
pickle.dump(model, open(model_file_path, 'wb'))

#Load the model 
model = pickle.load(open(model_file_path, 'rb'))

#Saving a Keras model
# Calling `save('my_model')` creates a SavedModel folder `my_model`.
model.save("my_model")
#Load a Keras Model
# It can be used to reconstruct the model identically.
reconstructed_model = keras.models.load_model("my_model")