Nikhil Dasari·3d agoProbability vs. Likelihood: The Subtle Difference That Is Easy to OverlookTo understand this, let’s start with a basic experiment we’re already familiar with.A response icon1A response icon1
Rayan Yassminh·Oct 17ML: Possible Is Not Probable, and Likely Is Not LikelihoodYour model just said 82%. Before you act on it, ask three different questions; not one.
Matthew Chequers, Ph.D.·Aug 24Mean and Standard Deviation Aren’t Just a Summary. Your Reader Assumes a Normal Distribution.Deriving them from scratch, and why two numbers alone are rarely the whole story
Yogeswari Lakshmiraman·Aug 3Machine Learning Series (Part 36) : Why Logistic Regression Uses Log Loss Instead of MSEGood day readers! In the last blog we saw about the sigmoid function and the way to calculate the maximum likelihood of logistic…
Yogeswari Lakshmiraman·Jul 12Machine Learning Series (Part 35) : How Logistic Regression Finds the Best S-Shaped CurveBonjour dear readers! We are in the journey of exploring logistic regression for the past two blogs. In the previous blog we saw logistic…
InDev GeniusbyPankaj Chejara, PhD·Jul 7How Likely Is Your Data? The Idea Behind Maximum Likelihood Estimation (MLE): Part-2What if your data could vote on the most plausible explanation? That’s exactly what MLE does.
InDev GeniusbyPankaj Chejara, PhD·Jul 7How Likely Is Your Data? The Idea Behind Maximum Likelihood Estimation (MLE)What if your data could vote on the most plausible explanation? That’s exactly what MLE does.A response icon2A response icon2
Yogeswari Lakshmiraman·Jun 29Machine Learning Series (Part 34) : How Logistic Regression Converts a Squiggle into a Straight…In part 33, we all saw what odds are, the log of the odds, the ratio of odds which is the odds ratio. I welcome you all to part 34 of our…
Leandre Sabourin·Jun 15How to Choose the Right SEM Estimator: ML, FIML, MLR, or WLSMV“This article is part of a series of articles on psychometrics and factor analysis. You can find all the other articles here: Factor…A response icon1A response icon1
Prateek Agrahari·Jun 12Day-4:The Probabilistic Soul of Machine Learning — Likelihood, MLE, and MAPEvery time you minimize a loss function, you are implicitly doing something probabilistic. Mean squared error is not just a convenient…
