Entropy

Definition of Entropy

Entropy: Entropy is a measure of the unpredictability of a system. In information theory, entropy ( ) is a measure of the uncertainty associated with a random variable. Entropy is defined as the average amount of information that is not known about the value of a random variable.

When is Entropy used?

Entropy is used when attempting to measure the uncertainty of a system or process. It is most commonly used in the context of information theory, where it is used to measure how much information is contained in a data set. Entropy can also be used to measure the degree of order or disorder in a system. In other words, entropy measures how much randomness or unpredictability exists in a system. In machine learning and data science, entropy is often used as a criterion for evaluating different models and algorithms. For example, decision tree algorithms are based on entropy and use it to determine which features are important for identifying patterns in datasets. Additionally, entropy can be used to assess whether the output of an algorithm matches what was expected. By comparing the expected values with actual values, one can evaluate how well an algorithm works and whether it should be improved upon or discarded entirely. This can help data scientists identify areas where they need additional refinement and optimization so that their models are more accurate and reliable.