Author(s): Anjali Kakde

Originally published on Towards AI.

The Loss Function That Punishes, for Being Confident and Wrong

For years, we were told that training a model meant minimizing error. Get the prediction right. Push accuracy higher. Reduce the loss.
But cross entropy was never designed to care about error in the way we intuitively think about it.

This article discusses cross-entropy as a loss function in machine learning that emphasizes measuring surprise rather than merely minimizing error. It explores the historical context of cross-entropy, linking it to information theory introduced by Claude Shannon. The article explains how cross-entropy assesses the confidence of predictions, punishing models for overconfidence in incorrect predictions while understanding the importance of uncertainty in real-world applications such as medical diagnoses and autonomous systems. It emphasizes that cross-entropy trains models to be humble and correctly calibrate their confidences, ultimately leading to better and more reliable decision-making systems.

Read the full blog for free on Medium.

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Published via Towards AI