Linked Questions

20
votes
2answers
6k views

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). ...
6
votes
2answers
8k views

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 ...
5
votes
2answers
1k views

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 ...
2
votes
1answer
155 views

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 ...
1
vote
0answers
304 views

Fitting of logit using least squares [duplicate]

So I am asked to fit a logit model using the method of least squares in connection to logistic regression. Let $\pi(x)=\mathrm{P}(Y=1|X=x)$ be the probability of success of a binary response variable $...
2
votes
0answers
232 views

Why using RMSE as loss function in logistic regression takes non convex form but doesn't in linear regression? [duplicate]

I am taking this deep learning course from Andrew NG. In the 3rd lecture of 2nd week of the first course, he mentions that we can use RMSE for logistic regression as well but it will take a nonconvex ...
0
votes
1answer
59 views

Why we do not use least squares in logit model? [duplicate]

I am very wondering why we do not use least squares instead of maximum likelihood? for example we have 3 choices k= 1, 2 ,3 $minimizing: (e^{\beta_{i} X}/(1+\sum e^{\beta_{i} X})- Y)^{2} $ for i=1,...
0
votes
0answers
37 views

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 ...
0
votes
0answers
17 views

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 ...
65
votes
18answers
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 ...
9
votes
2answers
5k views

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 ...
3
votes
2answers
1k views

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 ...
0
votes
1answer
941 views

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?
2
votes
2answers
209 views

why not Fitting GLMs with least squares?

My question is why we don't use least square to fit Generalized linear model parameters and instead always use maximum likelihood.
2
votes
1answer
192 views

Improving spam classification with tensorflow logistic regression

I would like to classify a mail (spam = 1/ham = 0), using logistic regression. My implementation is similar to this implementation and using tensorflow. A mail is represented as a bag-of-words ...

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