Support Vector Machines (SVM)

Support vector machines (SVMs) are a powerful tool for classification tasks, especially when the data is high-dimensional or the decision boundary is complex. In this lesson, we’ll explore the core concepts behind SVMs, implement an SVM classifier in Python for a cancer detection problem, and discuss ways to improve your model pipeline with additional features and tuning. Let’s get started.

Cancer classification with SVMs

Imagine you are working on a project to classify different types of cancer based on tumor characteristics. You have a dataset containing measurements of various tumor features, and your task is to classify whether a tumor is malignant or benign. Explain the basic concept of support vector machines (SVMs) and write a Python code snippet to create an SVM classifier using the sklearn library. Also explain how would you perform feature scaling and hyperparameter tuning?

Sample answer

The goal of an SVM is to find the best hyperplane that separates the data into different classes. For non-linearly separable data, SVMs use a kernel trick to map the data into a ...