Error metrics are quantitative measures used to assess the performance of predictive models. They help determine how well a model's predictions align with actual outcomes. Common error metrics include Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), each providing different insights into the model's accuracy. These metrics are essential in various fields, such as machine learning and statistics, as they guide model selection and improvement. By analyzing error metrics, data scientists can identify areas where a model may be underperforming and make necessary adjustments to enhance its predictive capabilities.