What does Entropy measure in data?

Entropy is a fundamental concept in information theory that quantifies the level of unpredictability or randomness in a dataset. When assessing a dataset, entropy helps to gauge how much uncertainty or disorder exists within the data. A higher entropy value indicates greater randomness, meaning there are more unpredictable outcomes, while lower values imply that the data is more ordered and predictable. This characteristic is particularly valuable in various fields, such as machine learning and data compression, where understanding the information content and structure of data can guide how models are built or how data is encoded.

In contrast, measuring the accuracy of predictions pertains more to the effectiveness of models in forecasting or classifying data rather than understanding the data itself. The amount of data stored is a matter of quantity rather than the intrinsic qualities of the data, and the efficiency of data processing relates to how well a system can handle and manipulate data, rather than the data's inherent unpredictability or disorder.