How to use logistic regression with poor goodness of fit?

How to use logistic regression with poor goodness of fit?

Logistic regression with poor goodness of fit (hosmer lemeshow)? – Cross Validated I built a model with 9 categorical predictor variables. Using SPSS, my omnibus test was significant ($\\chi^2$=220.01), my -2loglikelihood was 1335.2 (Nagelkerke $R^2$ 0.231), but my Hosmer and Leme… Stack Exchange Network

Why are recall and precision bad in logistic regression?

When I fit a logistic regression model on based dataset (using Smote for over sampling) , on training f1, recall and precision are good. But on testing, precision score and f1 are bad. I assume it makes sense because in training there were a lot more of the minority case while in reality/testing there is only very small percentage.

What are the observations of a logistic regression model?

Observations are around 120K. When I fit a logistic regression model on based dataset (using Smote for over sampling) , on training f1, recall and precision are good. But on testing, precision score and f1 are bad.

Is it bad to over predict positive instances in logistic regression?

That’s bad; it will cause the classifier to over-predict positive instances. For some classifiers, it’s not a major problem, but I expect that logistic regression might be more sensitive to this mismatch between training distribution and test distribution.

Do you need to calibrate a logistic regression model?

Note that if the model contains only categorical variables and interactions among the variables are not needed, the model mustfit the data and no calibration assessment is needed. To check co-linearities I suggest variable clustering, e.g., the R Hmiscpackage varclusfunction.

Which is the best strategy for logistic regression?

My Regression Modeling Strategies course notes at http://biostat.mc.vanderbilt.edu/rmspresent a hopefully coherent strategy for logistic regression model specification and validation. We almost never want to trade off predictive discrimination just to make a model have a better calibration curve.

Why is the link function misspecified in logistic regression?

A final possibility is that the relationship is linear, but on a different scale than the log odds (which is what logistic regression models). That is, the link function is misspecified. Note that this is a subtle form of the issue in #2 above.