Iterative Experimentation
Iterative Experimentation is a process of continuously testing and refining a hypothesis or model through multiple iterations. In Data Science, it involves the use of statistical methods and machine learning algorithms to analyze data and generate insights. The process starts with formulating a hypothesis or model based on available data and domain knowledge. The hypothesis is then tested on a subset of the data, and the results are analyzed to refine the hypothesis. This process is repeated multiple times until the hypothesis is validated or rejected. Iterative Experimentation is an essential part of the Data Science process as it allows for the discovery of new insights and the improvement of existing models. It also helps to reduce the risk of making incorrect decisions based on incomplete or inaccurate data.
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