The most common cost function in logistic regression is sum of squares mistake (also called residual sum of squares). Then, employing a calculus procedure generally known as gradient descent, the weights are regularly modified till the price operate is minimized.

The crucial element to logistic regression is schooling the model to assign proper weights to the many characteristics. This primary demands a cost functionality, which is simply a function that determines the extent of error of each and every prediction in comparison to the schooling example.

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Has tradeoffs with gradient descent. The speedier gradient descent determines the final function weights, the more most likely it can be to skip the best weights. It may in some cases be tough to determine this out the best speed compared to precision tradeoff, and trial-and-error might be time consuming

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