Save classifier to disk in scikit-learn #shorts

If you have trained a classifier using scikit-learn and you want to save it to disk for future use, you can easily do so using the joblib library. Here’s how:

1. First, make sure you have joblib installed. You can do this by running the following command:

pip install joblib

2. Next, import the necessary libraries in your Python script:


import joblib
from sklearn import svm


3. Train your classifier using scikit-learn, for example:


clf = svm.SVC()
X = [[0, 0], [1, 1]]
y = [0, 1]
clf.fit(X, y)


4. Save the classifier to disk using joblib:


joblib.dump(clf, 'classifier_model.pkl')


Now your classifier has been saved to a file called ‘classifier_model.pkl’ in the current directory. You can load it back into memory at any time by using the following code:


clf = joblib.load('classifier_model.pkl')


That’s it! You now know how to save a classifier to disk in scikit-learn using joblib. This can be useful for saving time and resources when working with large datasets or complex models. Happy coding!