Regression error metrics are measures of the performance of a regression model. These metrics allow us to evaluate the accuracy of the model’s predictions, and to compare the performance of different models. Some common regression error metrics include mean absolute error, mean squared error, and root mean squared error. These metrics are calculated by taking the difference between the predicted values and the true values, and then summarizing this difference using a summary statistic (such as the mean or the root mean squared). The choice of error metric will depend on the specific regression task and the preferences of the person using the model.
Evaluating performance is a crucial step in developing regression models. Because regression models return continuous outputs, such models allow for different gradations of right or wrong. Therefore, we measure the deviation between predictions and actual values in numerical terms. However, a universal metric to measure the performance of regression models does not exist. Instead, there … Read more
