Entropy and Information Theory

Entropy in information theory measures the uncertainty or unpredictability of a message or data set. It quantifies how much "surprise" or variability exists; higher entropy indicates more randomness, while lower entropy means more predictability. For example, a text with repeated words has low entropy, whereas a random string of characters has high entropy. This concept helps in data compression and transmission efficiency by identifying how much information is genuinely present. Overall, entropy provides a way to evaluate how much information is needed to describe or transmit data accurately.