November 2018
Intermediate to advanced
300 pages
7h 42m
English
Content preview from Machine Learning in Java - Second Edition
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Mean squared error
Mean squared error (MSE) is an average of the squared difference between the predicted and true values, as follows:
The measure is very sensitive to the outliers, for example, 99 exact predictions and 1 prediction off by 10 is scored the same as all predictions wrong by 1. Moreover, the measure is sensitive to the mean. Therefore, a relative squared error that compares the MSE of our predictor to the MSE of the mean predictor (which always predicts the mean value) is often used instead.
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