Description: The ‘reference value’ is a fundamental concept in model evaluation, especially in the field of data analytics and model optimization. It refers to a standard or point of comparison used to measure a model’s performance against other models or a specific target. This value can be an expected outcome, an average performance of previous models, or even a benchmark established by the industry. The importance of the reference value lies in its ability to provide a clear and quantifiable context that allows analysts and data scientists to assess the effectiveness of their models. Without a reference value, it is challenging to determine whether a model is performing adequately or needs adjustments. Additionally, using reference values facilitates the communication of results and findings, as it allows stakeholders to quickly understand how a model compares to others. In the context of data visualization tools, reference values are crucial for creating dashboards that display model performance in real-time, enabling users to quickly identify deviations and improvement opportunities.
