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In binomial logistic regression, we have cost function as: L(0) = = - yi log(he(xi)) + (1 y;) log(1 h(x;)) i=1 a) Express the
In binomial logistic regression, we have cost function as: L(0) = = - yi log(he(xi)) + (1 y;) log(1 h(x;)) i=1 a) Express the same for multinomial regression b) Show how gradient descent will be used. c) Can cross-entropy loss be used for logistic regression? If yes, how? d) How does cross-entropy handle multiple classes?
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Statistical Inference
Authors: George Casella, Roger L. Berger
2nd edition
0534243126, 978-0534243128
