HI, How do I check built Logistic regression model is suffering with BIAS or Variance.
I learnt about c-statistics, H-L Goodness -of-Fit Statistics, they all say how well the model is fit. But how can I interpret that the built model will perform better in unseen data. Please help to understand. Really struggling to get answer to this.
I don't understand your Q well.
1) if you want know if model is robust ,
You can use train data and test data to check model . or
n-fold cross validate method( split your data into ten group, using 9 of 10 to build model and 1 of 10 to test, and again using another 9 of 10 to build model and 1 of 10 to test , over and over again for ten times).
2)if you want know if data is over disperse. try
proc logistic......
model ..../scale=none aggregate ;
3) if your train data is sample from a big table . you need
model ............./ offset=
to adjust your predict probability .
I don't understand your Q well.
1) if you want know if model is robust ,
You can use train data and test data to check model . or
n-fold cross validate method( split your data into ten group, using 9 of 10 to build model and 1 of 10 to test, and again using another 9 of 10 to build model and 1 of 10 to test , over and over again for ten times).
2)if you want know if data is over disperse. try
proc logistic......
model ..../scale=none aggregate ;
3) if your train data is sample from a big table . you need
model ............./ offset=
to adjust your predict probability .
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