Return np.mean(np.abs((actual - pred) / actual)) * 100 There is no built-in Python function to calculate MAPE, but we can create a simple function to do so: import numpy as npĪctual, pred = np.array(actual), np.array(pred)
For example, a model with a MAPE of 5% is more accurate than a model with a MAPE of 10%.
The lower the value for MAPE, the better a model is able to predict values. For example, a MAPE value of 11.5% means that the average difference between the predicted value and the actual value is 11.5%. MAPE is commonly used because it’s easy to interpret and easy to explain. The mean absolute percentage error (MAPE) is commonly used to measure the predictive accuracy of models.
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