The website is in Maintenance mode. We are in the process of adding more features.
Any new bookmarks, comments, or user profiles made during this time will not be saved.

Machine Learning Resources

Why are coefficients estimated through Maximum Likelihood (MLE) instead of Least Squares?

Bookmark this question

The least squares cost function is non-convex in a binary classification setting, meaning the algorithm could get stuck in a local rather than global minimum and thus fail to optimize the loss. In addition, after the logit transformation is applied, the residuals, defined as the difference between the actual and predicted values, would be infinite, since the actual values are only either 0 or 1.

Leave your Comments and Suggestions below:

Please Login or Sign Up to leave a comment

Partner Ad  

Find out all the ways
that you can

Explore Questions by Topics

Partner Ad

Learn Data Science with Travis - your AI-powered tutor |