Imagine a dataset of with values for 10 features for 100,000 samples. Some feature values are missing at random from some samples.
I would like to use this incomplete data set to train a single model with the following characteristics:
- As input, this model will accept data from 1 sample that is incomplete for at least one of any of the features.
- As output, the model will predict scores (and provide measures of certainty in regards to those predictions) for all missing values.
Thanks in advance for your insights and ideas!