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.
Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.
Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via analytics, ads, other embedded contents are termed as non-necessary cookies. It is mandatory to procure user consent prior to running these cookies on your website.