L'entropie est un concept fondamental dans divers domaines, notamment thermodynamics, statistical mechanics, and théorie de l'information. It quantifies the amount of disorder or uncertainty within a system. In thermodynamics, entropy is often associated with the second law, which states that the total entropy of an isolated system can never decrease over time. This principle implies that natural processes tend to move towards a state de plus grand désordre ou d'aléa.
In the context of information theory, entropy measures the average amount of information produced by a stochastic source of data. More formally, it is defined as the valeur attendue of the information content of each possible outcome, often expressed in bits. Higher entropy indicates more unpredictability and a greater amount of information, while lower entropy suggests that the data is more predictable and contains less information.
Entropy has significant implications across various domains, from predicting the behavior of gases in physics to understanding the complexity of messages in systèmes de communication. In machine learning and AI, entropy is often used in algorithms to evaluate information gain, guiding the selection of features or the splitting of nodes in decision trees.