Gradient Of Logistic Regression Cost Function. Log Loss is the most important classification metric based on probabilities. J 1msum-y logh - 1 - y log1-h.
Grad X hx - y m. Grad should have the same dimensions as theta h sigmoidXtheta. If y 1.
One for y1 and one for y0.
Cost of gradient step is high use stochastic gradient descent Carlos Guestrin 2005. But as h θ x -. One for y1 and one for y0. The procedure is similar to what we did for linear regression.
