Cross Entropy vs MSE: Which Loss Function is Best for Your Model?
In machine learning, the loss function plays a central role: it measures how well a model is performing and guides optimization during training. Two commonly used loss functions are Mean Squared Error (MSE) and Cross Entropy. While they sometimes appear interchangeable, they are designed for different types of problems. Choosing the right one can significantly affect model performance.
In this post, we’ll break down what each loss function is, how it works, and when to use it.
