Expressed as a percentage, which is scale-independent and can be used for comparing forecasts on different scales.The formula often includes multiplying the value by 100%, to express the number as a percentage. In this case, we can interpret t as either observation in case we are doing a generic regression problem (predicting the weight of a person or the price of a house) or as the time index in the case of time series analysis. Where A_t stands for the actual value, while F_t is the forecast. The mean absolute percentage error is one of the most popular metrics for evaluating the forecasting performance. That is why in this article I cover two of the metrics I have recently worked with. ![]() This way, we can choose the metric most suitable for the task at hand. I believe that the key to answering this question is knowing the strengths and limitations of the most popular metrics. ![]() ![]() So how to decide which metric to use for our projects? ![]() There is a vast ocean of different error metrics out there, each one with its set of pros and cons and supposedly covering more cases than the previous ones. MSE, RMSE, MAE, MAPE, sMAPEā¦ to name just a few. Image by Pexels from Pixabay The pros and cons of two popular error metrics
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