# How do I compute log-likelihood for training set in supervised learning?

I am building a supervised learning model and I wish to compute the log-likelihood for the training set at the point of the minimum validation error.

Initially, I was computing the sum of all the probabilities with maximum value obtained after applying softmax for each example in the training set at the point of minimum validation error but that doesn't look correct.

What is the correct formula for the log-likelihood?

• I too thought on the similar lines. I thought of using negative loss likelihood loss as given here. But this gives us negative log-likelihood loss whereas we are interested in calculating only log-likelihood. Could you please clarify my doubt? – Akhilesh Pandey Aug 9 '18 at 6:16