Data Science Interview Deep Dive: Cross-Entropy Loss

What makes it the de-facto multi-class classifier loss function

There is much to learn and grasp to ace that important Data Science/Machine Learning interview. This article is part of a series of articles that try to make the preparation process easier and less daunting by introducing structure, use visual explanations, and keeping things relevant.

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Motivation

The proper choice of loss functions is essential to train your machine learning models successfully. Thus questions about them are often brought up by the recruiters during the interview process. Questions may come from multiple fronts and angles, yet as long as you grasp the core concept, you will find that they won’t be much of a challenge. The cross-entropy loss is one of the most essential and widely used loss functions for multi-category classification problems. Why is it so popular? What are the core benefits? How to apply them properly?

Let’s dive in.

Explain it in One Minute

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