Cross-Entropy in Data Science: Meaning and Use
Cross Entropy in Data Science is a key concept used to measure the difference between two probability distributions the actual values and the predicted values from a model. It is widely used as a loss function in classification tasks, especially in neural networks, to evaluate how well a model predicts outcomes. As one of the essential Data Science Concepts, cross-entropy helps optimize model performance by penalizing incorrect predictions, thereby improving accuracy and reliability in machine learning applications.