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The choice of the associated fee function is yet another vital piece of an ML method. In different contexts, staying “Erroneous” can imply quite various things. In our employee satisfaction case in point, the very well-recognized standard could be the linear least squares perform:
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How can we practice a device to flawlessly predict an employee’s degree of fulfillment? The solution, needless to say, is we could’t. The purpose of ML isn't to create “fantastic” guesses for the reason that ML specials in domains the place there isn't any such issue. The aim is for making guesses which have been adequate to become valuable.
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