Maximum Likelihood Estimation in Logistic Regression

Then we rotate the line a little bit and do the same. The line with the smallest sum of squared residuals is the line chosen to fit best. <h2>Why can’t we make use of least-squares to find the best fitting line in logistic regression?</h2> Well, to answer this we need to recall logistic regression. Our goal in logistic regression is to draw the best fitting S-curve for given data points. And in logistic regression, we transform the y-axis from the probabilities to log(odds). The problem is that this transformation pushes the data points to positive and negative infinity as shown below <a href="https://arunaddagatla.medium.com/maximum-likelihood-estimation-in-logistic-regression-f86ff1627b67">Read More</a>