What are the steps to implement multi-class classification using SVM in scikit-learn

Question

Can you explain to me What are the steps to implement multi-class classification using SVM in scikit-learn?

Answer 1

To implement multi-class classification using SVM in Scikit-Learn, use the SVC classifier with the one-vs-one (OvO) or one-vs-all (OvA) strategy, which automatically handles multiple classes.

Here is the code snippet you can refer to:

In the above code we are using the following key approaches:

• Dataset Selection: Uses the Iris dataset, which has 3 classes (multi-class classification).
• Data Splitting: Divides the dataset into training (80%) and testing (20%) sets.
• SVM Model Configuration: Uses the RBF kernel with One-vs-Rest (OvR) multi-class handling.
• Performance Evaluation: Computes accuracy, classification report, and confusion matrix for analysis.
• Visualization: Displays the confusion matrix to assess model performance visually.

Hence, SVM efficiently handles multi-class classification using the one-vs-rest strategy, achieving strong predictive performance with proper hyperparameter tuning.