Can you explain this question

#1

Q8. Likelihood Estimation in Logistic Regression
Suppose, there are 6 training (x1…x6) points, and hypothesis value comes out to be as follows -
Remember hθ(x) = sigmoid(wT.x + b), where w and b are weights and bias learned by the model after training.
hθ(x1) = 0.6
hθ(x2) = 0.4
hθ(x3) = 0.7
hθ(x4) = 0.1
hθ(x5) = 0.4
hθ(x6) = 0.9
The maximum likelihood estimation is defined as the product of the probabilities p(yi|xi). Assuming 100% correct classification, the likelihood value(without log) for the above case would be -

0.006048

0.122472

0.198762

Class Labels are needed

#2

Hey @mehta.rashita18, since it is mentioned that 100% correct classification is done, so you can make class labels yourself, like this

1 (2)
1 (2)1280×960 110 KB

And finally you need to take product of all values in the last column, to get the answer.

Hope this cleared your doubt.
Don’t forget to mark the doubt as resolved in my doubts section. :blush:

#3

I hope I’ve cleared your doubt. I ask you to please rate your experience here
Your feedback is very important. It helps us improve our platform and hence provide you
the learning experience you deserve.

On the off chance, you still have some questions or not find the answers satisfactory, you may reopen
the doubt.