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# ML | Cost function in Logistic Regression

• Last Updated : 06 May, 2019

In the case of Linear Regression, the Cost function is – But for Logistic Regression, It will result in a non-convex cost function. But this results in cost function with local optima’s which is a very big problem for Gradient Descent to compute the global optima. So, for Logistic Regression the cost function is If y = 1 Cost = 0 if y = 1, hθ(x) = 1
But as,
hθ(x) -> 0
Cost -> Infinity

If y = 0 So,   To fit parameter θ, J(θ) has to be minimized and for that Gradient Descent is required.

Gradient Descent – Looks similar to that of Linear Regression but the difference lies in the hypothesis hθ(x) Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.

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