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How To Save & Reload a Python Machine Learning Model using Pickle ?

In this post lets understand How To Save a Machine Learning Model in Python using Pickle ? Pickle is used quite often in Python especially in Machine Learning modelling. We will try to understand what is & How to use Pickle.  

What is Pickle

  • Pickle is a Standard way of serializing objects in Python. It can be a Machine Learning Algorithm or any other Object.
  • You can serialize and save the model or Object using Pickle . It is saved in a serialized format as a file.
  • When you need to re-use or re-load the same Model or Object , you can reload and de-serialize the file using Pickle.

Example Code


import numpy as np
from sklearn import datasets
iris \= datasets.load\_iris()
iris\_X \=
iris\_y \=

\# Split iris data in train and test data
\# A random permutation, to split the data randomly
indices \= np.random.permutation(len(iris\_X))
iris\_X\_train \= iris\_X\[indices\[:\-10\]\]
iris\_y\_train \= iris\_y\[indices\[:\-10\]\]
iris\_X\_test \= iris\_X\[indices\[\-10:\]\]
iris\_y\_test \= iris\_y\[indices\[\-10:\]\]
\# Create and fit a nearest-neighbor classifier
from sklearn.neighbors import KNeighborsClassifier
knn\_model \= KNeighborsClassifier()
knn\\_X\_train, iris\_y\_train) 

Create a Pickle File

  • Our Objecctive is to create a Pickle file of the TRAINED model - knn_model in this case**.** That is we will save the model as a serialized object using Pickle.
  • Use the code below -

\# save the knn\_model to disk
filename = 'Our\_Trained\_knn\_model.sav'
pickle.dump(knn\_model, open(filename, 'wb'))

  • We have saved our Trained model knn_model as file Our_Trained_knn_model.sav

Re-load the Pickle File

  • Our objective is to re-load the Pickle file from disk, De-serialize it and convert it back to our Knn Model to do prediction .
  • All these objectives can be achieved with Pickle
  • Code is below -

\# load the model from disk
filename = 'Our\_Trained\_knn\_model.sav'
knn\_model\_reloaded = pickle.load(open(filename, 'rb'))




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