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Least squares logistic regression [duplicate]

I have seen it claimed in Hosmer & Lemeshow (and elsewhere) that least squares parameter estimation in logistic regression is suboptimal (does not lead to a minimum variance unbiased estimator). ...
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why sum of squared errors for logistic regression not used and instead maximum likelihood estimation is used to fit the model? [duplicate]

I have a doubt on why sum of squared errors is not used for Logistic regression and instead maximum likelihood estimation is used and also why not the vice versa. Edited Many were asking me to ...
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Logistic regression cost surface not convex [duplicate]

I am building a simple logistic regression model on 2D data. Here is the input I use. I built a logistic regression model using this data and it successfully is able to find the discriminating line ...
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is cost function of logistic regression convex or not? [duplicate]

For logistic regression, the loss function is convex or not? Andrew Ng of Coursera said it is convex but in NPTEL it is said is said it is non convex because there is no unique solution. (many ...
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Logistic Regression For Classification [duplicate]

The origin of logistic regression is actually logistic curve which varies from the value 0 to the value 1. It looks like the letter S, and it specifies the growth of species. If our data distribution ...
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Estimating a logistic regression with OLS? [duplicate]

NB: This question is different from this one which assumes that we have computed the LHS of the regression equation with no issue. My question is about how to compute this LHS. Consider a simple ...
88k views

Statistics interview questions

I am looking for some statistics (and probability, I guess) interview questions, from the most basic through the more advanced. Answers are not necessary (although links to specific questions on this ...
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How To Solve Logistic Regression Using Ordinary Least Squares?

I was self-learning machine learning. I came upon this section of the Wikipedia page on Logistic regression, where it claims Because the model can be expressed as a generalized linear model (see ...
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Why use different cost function for linear and logistic regression?

I mean least squares already penalize one big mistake more, then several small ones. So why don't just leave same "mean square error" for logistic regression - it is simpler than messy formula with ...
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How to perform classification if you had to use linear regression?

If you had to use linear regression for classification, how would you achieve this?