Iterative Cycle
Iterative cycle is a process of repeating a set of steps or tasks until a desired outcome is achieved. In data science, an iterative cycle is often used in the development of machine learning models. The process involves several steps such as data collection, data preprocessing, model selection, model training, and model evaluation. After the initial model is built, it is evaluated and refined through multiple iterations until the desired level of accuracy is achieved. The iterative cycle allows data scientists to continuously improve the model by incorporating new data, adjusting parameters, and refining the algorithms. This process is also used in agile software development, where the development team works in short cycles to continuously improve the software product.
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