These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.
Mean Squared Error (MSE)
In statistics, the mean squared error (MSE) or mean squared deviation (MSD) of an estimator (of a procedure for estimating an unobserved quantity) measures the average of the squares of the errors—that is, the average squared difference between the estimated values and the actual value. MSE is a risk function, corresponding to the expected value of the squared error loss.
MSE connects to Robustness because it quantifies the average error between predicted and actual values, allowing practitioners to assess how reliably the model performs under normal conditions. Consistently low MSE across different datasets or scenarios can indicate that the model maintains its performance and is less likely to fail unexpectedly, which is a key aspect of robustness.
Related use cases :
Deep learning for minimum mean-square error approaches to speech enhancement
Uploaded on Nov 1, 2022Recently, the focus of speech enhancement research has shifted from minimum mean-square error (MMSE) approaches, like the MMSE short-time spectral amplitude (MMSE-STSA) estimat...
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