Let say I'm building a model to predict the number of accidents in a insurance portfolio of automobiles. The problem is that my model is is very very sensitive to the seed (for each time I change the seed, the significance of each predictor changes) (this is due to the fact that there is only a small percentage of accidents among the portfolio). So I come up with an "invention": I change the seed 1000 times, and I calculate the empirical probability of significance at level of 5% for each predictor. By doing that, I will know that what predictor is really significant (for example, with a probability of significance at level of 5% greater than 70%). This "invention" is really similar to the idea of bagging, but I'm not sure that it is correct or not? Can someone help explain to me?


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.