I might be missing something, but if you have sets of data where some of it is missing, but you have other data sets where it exists / is complete, then why not train a network with the data you have where it's complete, in order to predict the data where it's not? i.e. a machine learning solution to a machine learning problem.
Hi mam , i am also a Ph.D student doing in agricultural statistics in IASRI. I need to know building models for missing data. Can you send me your mail id. Because I need some help from you mam.
Thank you for the lecture, could you kindly reccomend what to read on developing dynamic models for longitudinal data with missing values?
Thank you, this was very helpful to understand the types of missing data and how to deal !
See the complete SciPy 2016 Conference talk & tutorial playlist here: ua-cam.com/play/PLYx7XA2nY5Gf37zYZMw6OqGFRPjB1jCy6.html
Very Interesting, both the presentation and the questions were Helpful !
Thank you!
I might be missing something, but if you have sets of data where some of it is missing, but you have other data sets where it exists / is complete, then why not train a network with the data you have where it's complete, in order to predict the data where it's not? i.e. a machine learning solution to a machine learning problem.
Thank you! This is awesome. Good questions too.
Hi mam , i am also a Ph.D student doing in agricultural statistics in IASRI. I need to know building models for missing data. Can you send me your mail id. Because I need some help from you mam.
Hi to my classmates from IBA101Hh.