I have a logistic model that should predict a defect (
1=defect, 0=no defect). My model uses 4 out of 14 parameters, which are significant for my dependent variable (tested through
summary() and the
anova() chi-squared test). Furthermore, I used 80% (~5000, with ~1200 defects) of my data to train the model and 20% (~1200, ~300 defects) to test it.
My results are:
If the cutoff I use is 50%*
- Sensitivity = 23% (percent of correctly predicted defects/total number of actual defects)
- Specificity = 98% (percent of correctly predicted non defects/total number of non defects)
- Accuracy = 80% (ratio of correctly predicted units/total number of units)
If the cutoff I use is the percent of defects in the training set [i.e., training defects / all training rows (~1200/~5000 = ~24%)]
- Sensitivity = 55%
- Specificity = 81%
- Accuracy = 75%
My question is:
How can I train my model to get a better result for my sensitivity (defect rate)? (Or is there any fault in my approach, i.e. how can i check my model for scientific correctness??)
I'm somehow lost at this point and appreciate any help, reference to a book or link that guides me in the right direction.