超越均值：数据科学中误差指标的战略指南

In the realm of predictive modeling, assessing model performance goes beyond simply asking, 'Is the model working?' Selecting the right error metrics is crucial for understanding a model's performance. Various error metrics can reveal different strengths and weaknesses of a model, aiding data scientists in making informed decisions. The article delves into several error metrics, including mean squared error, absolute error, and relative error, analyzing their applicability in different scenarios. By grasping these metrics, data scientists can more effectively optimize models, enhancing the accuracy and reliability of predictions.