This type of question tests your understanding of how to communicate complex and technical nuances with poise and the ability to summarize quickly and efficiently. Make sure you have a choice and make sure you can explain different algorithms so simply and effectively that a five-year-old could grasp the basics!
When asked about your favorite algorithm in a machine learning interview, it’s essential to choose an algorithm that you are genuinely comfortable with and can explain concisely. Here’s an example response for the k-nearest neighbors (KNN) algorithm:
“My favorite algorithm is k-nearest neighbors, or KNN. It’s a simple yet powerful supervised learning algorithm used for classification and regression tasks. In less than a minute, here’s how it works: Given a dataset with labeled examples, KNN classifies new data points by finding the ‘k’ nearest neighbors based on a chosen distance metric, typically Euclidean distance. For classification, the majority class among the k neighbors determines the class of the new data point. For regression, KNN computes the average or weighted average of the target values of the k neighbors to predict the target value for the new data point. It’s intuitive, easy to implement, and doesn’t require training, making it suitable for quick prototyping or baseline models.”