Regression Metrics are a set of techniques used to evaluate the performance of a regression model, which is a type of machine learning model that is used to predict a continuous numerical value. These metrics provide a way to measure the accuracy and other aspects of a model’s performance, such as how well it can predict future values.

In Python, the popular library scikit-learn provides a number of built-in functions for calculating regression metrics such as mean_absolute_error, mean_squared_error, r2_score, among others. Each of these functions calculates a different metric, and they can all be used to evaluate the performance of a regression model on a dataset.

Mean Absolute Error (MAE) is the average of the absolute differences between the predicted and actual values. It gives an idea of the magnitude of the error, but not the direction.

Mean Squared Error (MSE) is the average of the square of the differences between the predicted and actual values. It gives an idea of the magnitude of the error, but also the direction.

R2 Score or coefficient of determination is a statistical measure that represents the proportion of the variance in the dependent variable that is predictable from the independent variable. This metric ranges from 0 to 1, the higher the score, the better the model’s performance.

Root Mean Squared Error (RMSE) is the square root of the mean squared error, it gives an idea of the absolute magnitude of the error in the units of the output variable.

In addition to these metrics, other useful regression metrics include explained variance score and mean absolute percentage error (MAPE).

Explained variance score measures the proportion to which a mathematical model accounts for the variation (dispersion) of a given data set.

Mean Absolute Percentage Error (MAPE) gives the percentage deviation of the forecasted value from the actual value.

In conclusion, Regression metrics are a set of techniques used to evaluate the performance of a regression model. Scikit-learn, a python library provide us with several built-in functions for calculating various regression metrics such as mean absolute error, mean squared error, R2 score, Root mean squared error, explained variance score and mean absolute percentage error among others. These metrics allow us to evaluate the effectiveness of a model and make informed decisions about which model to use for a given problem.