performance metrics for regression problems we have discussed regression and its algorithms in previous herewe are going to discuss various performance metrics that can be used to evaluate predictions for regression problems mean absolute error (maeit is the simplest error metric used in regression problems it is basically the sum of average of the absolute difference between the predicted and actual values in simple wordswith maewe can get an idea of how wrong the predictions were mae does not indicate the direction of the model no indication about underperformance or overperformance of the model the following is the formula to calculate maemae | yn herey=actual output values and predicted output values we can use mean_absolute_error function of sklearn metrics to compute mae mean square error (msemse is like the maebut the only difference is that the it squares the difference of actual and predicted output values before summing them all instead of using the absolute value the difference can be noticed in the following equationmse ( yn herey=actual output values and predicted output values we can use mean_squared_error function of sklearn metrics to compute mse squared ( squared metric is generally used for explanatory purpose and provides an indication of the goodness or fit of set of predicted output values to the actual output values the following formula will help us understanding it = ) ( - = ) ( - = in the above equationnumerator is mse and the denominator is the variance in values we can use _score function of sklearn metrics to compute squared value