See the attached and the references.for full.details. Best wishes David Booth

Assuming you are using a logistic regression model with a binary outcome, you might determine how often the model predicts the right outcome. See "count" pseudo r-squared here:

https://stats.oarc.ucla.edu/other/mult-pkg/faq/general/faq-what-are-pseudo-r-squareds/

You might check your model for overdispersion.

If you are comparing among models, you might look at AIC, AICc, or BIC.

Breusch-Pagan, VIF, Durbin-Watson or Breusch-Godfrey, and regression on the residuals. Then LR test on a model that does not include your key explanatory variable against one that does.

Check VIF. Select your variables using the values of t. There are many other measures to look at for model accuracy. AIC, BIC, and S measures actually reflect the fitment of models. More important is what the business needs from the model. It’s not always the statistical inferences but business inferences are equally important

Anuraj Nayarisseri which value of AIC and BIC should I consider as accurate? I have little knowledge of it that a lower AIC value implies an accurate model.

Nitin Sharma, You may consider the lowest AIC as you run more and more iterations. And make sure that the Test set and Training set variance should not exceed 10% for RMSE.

With this program you can do logistic regression, and it uses a Monte Carlo method to estimate the reliability:

www.lerenisplezant.be/fitting.htm