How can I serialize scikit-learn pipelines?

How can I serialize scikit-learn pipelines?

You can serialize scikit-learn pipelines using the joblib or pickle libraries. Which one would you like to know more about?

What's the difference between joblib and pickle?

joblib is more efficient for large numpy arrays, while pickle is more general-purpose. For scikit-learn, joblib is often recommended.

Can you show me an example of using joblib to serialize a pipeline?

Sure! Here's a simple example:

from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
import joblib

# Load data
X, y = load_iris(return_X_y=True)

# Create a pipeline
pipeline = Pipeline([('classifier', RandomForestClassifier())])

# Fit the pipeline
pipeline.fit(X, y)

# Serialize the pipeline
joblib.dump(pipeline, 'pipeline.pkl')

This saves the pipeline to a file named 'pipeline.pkl'.

How do I load the serialized pipeline back?

You can load the serialized pipeline using joblib.load(). Here's how:

loaded_pipeline = joblib.load('pipeline.pkl')

Now you can use loaded_pipeline to make predictions!