THE DEFINITIVE GUIDE

How to save and load your Scikit-learn models in a minute

Save the pickle

Have you ever built a Machine learning Model and wondered how to save them? Well in a minute, I will show you how to save your Scikit learn models as a file.

The saving of data is called Serialization, where we store an object as a stream of bytes to save on a disk. Loading or restoring the model is called Deserialization, where we restore the stream of bytes from the disk back to the Python object.

Reasons why you should save your model?

1. In case you need to recreate the Trained model.
2. Share the model with others. We can save the model onto a file and share the file with others, which can be loaded to make predictions.
3. When you need to use the model for production purposes. To avoid long training times, We have trained the model on a huge data set and have a well-performing predictive model.

Tools to save and restore models in Scikit-learn

The first tool we describe is Pickle, the standard Python tool for object serialization and deserialization. Afterwards, we look at the Joblib library which offers easy (de)serialization of objects containing large data arrays, and finally, we present a manual approach for saving and restoring objects…