Cross-Entropy Loss

Cross-entropy loss is a widely used loss function in machine learning, particularly for classification tasks. It measures the difference between two probability distributions: the predicted probabilities from the model and the actual distribution of the labels. The main characteristic of cross-entropy loss is that it penalizes incorrect predictions more heavily, encouraging the model to output probabilities closer to the true labels. This loss function is commonly used in training neural networks, especially in tasks like image recognition and natural language processing, where classification accuracy is crucial. Its effectiveness in handling multi-class problems makes it a preferred choice among data scientists and machine learning practitioners.