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Save classifier to disk in scikit-learn
Scikit-learn is a popular machine learning library in Python that allows users to easily build and train various machine learning models. One common task when working with machine learning models is saving the trained model to disk so that it can be used later without having to retrain it again. In this article, we will discuss how to save a classifier to disk in scikit-learn.
Step 1: Train your classifier
Before saving a classifier to disk, you first need to train it on your data. This typically involves loading your dataset, splitting it into training and testing sets, and then fitting the classifier to the training data. For example, you may use a RandomForestClassifier from scikit-learn to train a classifier on your data.
“`python
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
clf.fit(X_train, y_train)
“`
Step 2: Save the classifier to disk
Once you have trained your classifier, you can save it to disk using the joblib library in scikit-learn. Joblib is a lightweight library for serializing Python objects, and it is commonly used to save scikit-learn models.
“`python
import joblib
joblib.dump(clf, ‘classifier_model.pkl’)
“`
This code snippet saves the trained classifier ‘clf’ to a file named ‘classifier_model.pkl’ in the current working directory. You can choose a different filename or location to save the model as needed.
Step 3: Load the classifier from disk
Once you have saved your classifier to disk, you can easily load it back into memory and use it for making predictions on new data. This can be done using the joblib.load() function in scikit-learn.
“`python
clf = joblib.load(‘classifier_model.pkl’)
“`
Now that you have loaded the classifier back into memory, you can use it to make predictions on new data without having to retrain the model from scratch.
Conclusion
Saving a classifier to disk in scikit-learn is a simple process that can save you time and resources in the long run. By following the steps outlined in this article, you can easily save your trained model to disk and load it back into memory for reuse in future projects.
