Write classifier to disk using scikit-learn

In scikit-learn, you can easily save a trained classifier to disk for later use. This can be useful if you want to reuse your trained model without having to retrain it every time. In this tutorial, I will show you how to save a classifier to disk using scikit-learn and Python.

To get started, make sure you have scikit-learn installed in your Python environment. You can install it using pip:

pip install scikit-learn

Now, let’s start by training a classifier using scikit-learn. For this tutorial, I will use a simple example of training a Support Vector Machine (SVM) classifier on the famous Iris dataset.

First, let’s import the necessary libraries and load the Iris dataset:

from sklearn import svm
from sklearn import datasets

# Load the Iris dataset
iris = datasets.load_iris()
X, y = iris.data, iris.target

Next, let’s train the SVM classifier on the Iris dataset:

clf = svm.SVC()
clf.fit(X, y)

Now that we have trained our classifier, we can save it to disk using the joblib module from scikit-learn. joblib is a library for serializing Python objects to disk. We can use it to save our trained classifier as follows:

from joblib import dump

# Save the classifier to disk
dump(clf, 'svm_classifier.joblib')

In the code above, we used the dump function from the joblib module to save the trained SVM classifier to a file named svm_classifier.joblib.

To load the saved classifier from disk and use it for predictions, you can use the load function from the joblib module:

from joblib import load

# Load the classifier from disk
clf_loaded = load('svm_classifier.joblib')

# Make predictions using the loaded classifier
predictions = clf_loaded.predict(X)

# Print the predictions
print(predictions)

That’s it! Now you know how to save a trained classifier to disk in scikit-learn. You can use this technique to save any trained classifier in scikit-learn for later use. Remember to always save your classifiers to disk after training them, so you can reuse them without having to retrain them every time.