Jun 30, 2023 · In this forum, there are opposite opinions(1), (2) on the uses of logistic regression. Ones say, it is a classification model and others say it is a prediction model. Therefore, the question that I...

Mar 3, 2014 · In linear regression Y Y is a continuous dependent variable, but in logistic regression it is regressing for the probability of a categorical outcome (for example 0 and 1).

This looks like it's saying that logistic regression tends to overfit when you add many parameters to the model, which regularization helps combat.

Dec 25, 2016 · 8 I would like to assess the goodness of fit of a logistic regression model I'm working on. I've done a lot of research and happened to find likelihood ratio test, chi-squared test, Hosmer and …

Dec 8, 2014 · 121 Since Logistic Regression is a statistical classification model dealing with categorical dependent variables, why isn't it called Logistic Classification? Shouldn't the "Regression" name be …

For logistic regression models unbalanced training data affects only the estimate of the model intercept (although this of course skews all the predicted probabilities, which in turn compromises your …

In my book Regression Modeling Strategies I downplay the use of residuals in logistic regression because (1) logistic regression makes no distributional assumptions and (2) there are more direct …

This one is good for capturing things like ambiversion. Logistic regression works with both - continuous variables and categorical (encoded as dummy variables), so you can directly run logistic regression …

73 I have SPSS output for a logistic regression model. The output reports two measures for the model fit, Cox & Snell and Nagelkerke. So as a rule of thumb, which of these R2 R ² measures would you …

Jun 14, 2015 · In general, the log odds ratios of a logistic regression model tend toward a biased factor of 2β 2 β because of non-collapsibility of the odds ratio and zero cell counts. In inference, this is …