Description: Error metric is a measure used to evaluate the accuracy of a model, especially in the context of statistics and machine learning. This metric allows quantifying the discrepancy between the values predicted by a model and the actual observed values. There are various error metrics, each with its characteristics and specific applications, such as mean squared error (MSE), mean absolute error (MAE), and accuracy, among others. Choosing the right metric is crucial, as it influences the interpretation of results and decision-making regarding the model’s effectiveness. In the field of data visualization, tools allow for graphing these error metrics, facilitating the understanding of the model’s performance through visual representations. The error metric not only helps identify the quality of a model but is also essential for parameter optimization and tuning, thereby contributing to improving the accuracy and robustness of the predictions made by the model.
