Even the worthy Homer sometimes nods. The idiom means even the most gifted person occasionally makes mistakes. We would adapt this sentence to machine learning lifecycle. Even the best ML-models should make mistakes (or else overfitting problem). The important thing is know how to measeure errors. There are lots of metrics for measuring forecasts. In this post, we will mention evalution metrics meaningful for ML studies.
Sign of actual and predicted value diffence should not be considered when calculation total error of a system. Otherwise, total error of a series including equally high underestimations and overestimations might measure very low error. In fact, forecasts should include low underestimations and overestimations, and total error should be measured low. Discarding sign values provides to get rid of this negative effect. Squaring differences enables discarding signs. This metric is called as Mean Squared Error or mostly MSE.