Why Binary Cross-Entropy Matters: A Guide for Data Scientists

Author(s): Niklas Lang

Originally published on Towards AI.

The Mathematics, Intuition, and Implementation of BCE in ML

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For machine learning models to learn and improve their predictions, they need so-called loss functions that measure how large the difference is between the prediction and the actual value. Binary cross-entropy (BCE) is a central loss function used for binary classifications, i.e. those that assign objects to one of two classes. It helps to train models precisely and reliably, whether in the recognition of spam emails or the medical diagnosis of patients.

In this article, we will take a basic look at what loss functions are and how binary cross-entropy works. We will also take a detailed look at the mathematical structure of the function and explain it using a simple example. Furthermore, we examine the various areas of application in which BCE is used and highlight the advantages and disadvantages of this loss function.

The loss function, also known as the objective function or cost function, is a crucial concept in machine learning that evaluates the performance of the training model and guides the learning process. Put simply, it calculates the difference between the actual target values in the data set and the model’s prediction…. Read the full blog for free on Medium.

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Published via Towards AI