Exploration And Exploitation


Exploration and exploitation is a concept in data science that refers to the trade-off between trying out new things (exploration) and using what is already known to be effective (exploitation). In the context of machine learning, exploration involves trying out new models or algorithms to see if they can improve performance, while exploitation involves using the best-performing model or algorithm to make predictions. The goal is to find a balance between exploration and exploitation that maximizes overall performance. This concept is particularly important in reinforcement learning, where an agent must decide whether to explore new actions or exploit actions that have already been tried. In business, exploration and exploitation can refer to the balance between developing new products or services and optimizing existing ones.


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