Following the theoretical part is a practical one - namely, building a SVM classifier for binary classification This answers the question _How to create a binary SVM classifier?_ We will be using ...

Finally, after implementing SVM for multiclass classification problems, you need to evaluate the performance of the model using some metrics, such as accuracy, precision, recall, or F1-score.

Building the SVM classifier: we're going to explore the concept of a kernel, followed by constructing the SVM classifier with Scikit-learn. Using the SVM to predict new data samples: once the SVM is ...