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Hi Junyuan,

Interesting topic, and very easy to understand.

I have some suggestions and questions here:

1. Mice actually does not always perform as satisfying as your graph though. Can you explain more on why do you choose MICE as a comparing target? Say like how Mice performs nicely in similar circumstances.

2.I think your solution of neural network is very inspiring and reasonable, I think maybe you just chose the wrong data sets for testing, if you test on the cases that have more instances, the result would be much more acceptable.

3.In your “master” investigation part, do you mean that you have only 53 instances for this training? Or you also have others? why there are also people with other titles in your result? Did I miss anything?

I still do not want believe that your hypothesis is wrong.

05:03, 21 April 2016

Hi Dandan,

Thank you for your feedback!

1.The main reason that I use MICE is it is very easy to implement, and most people in the competition is using the MICE+R to fill the missing value.

3. Yes, I use the full dataset first (with 5 different title classes), but the single neural network & neural network ensemble model result is not good. Therefore, I want to investigate why the neural network ensemble cannot improve the performance. So in the further investigation section, I implement a single neural network and using a dataset that only contain Master title, which contains 53 instances.

Hope this will answer your question.



Junyuan Zheng

06:26, 21 April 2016