MLE – Machine Learning Interviews

What is the Maximum Likelihood Estimate (MLE)?

Posted on March 22, 2021August 5, 2021 by MLNerds

Probabilistic Models help us capture the inherant uncertainity in real life situations. Examples of probabilistic models are Logistic Regression, Naive Bayes Classifier and so on.. Typically we fit (find parameters) of such probabilistic models from the training data, and estimate the parameters. The learnt model can then be used on unseen data to make predictions….

With the maximum likelihood estimate are we guaranteed to find a global Optima ?

Posted on February 27, 2019July 1, 2019 by MLInterview

Maximum likelihood estimate finds that value of parameters that maximize the likelihood. If the likelihood is strictly concave(or negative of likelihood is strictly convex), we are guaranteed to find a unique optimum. This is usually not the case and we end up finding a local optima. Hence, the Maximum likelihood estimate usually finds a local…