I am currently working on a project that has me stumped about the difference between imputing a "missing value" and predicting an "unknown value".
So far I would understand imputation as the process of filling in gaps in data that were created due to the measurement process e.g. data that should have been known but was not measured along with anything else. Mostly this meant that the gaps in data - in my mind - didn't split the data set in a "complete training sample" and an "incomplete test sample".
Predictive models conversely, I used to predict unknown values based on data I had measured. These values were unknown because I could not measure them (e.g. because it was future data) but a complete data sample (e.g. with historic data) was available thus splitting data into a "training sample" and a "test sample".
However my current problem is blurring the lines between these two ideas.
Assuming a sample of products with known attributes (X1,X2,X3, etc.), I want to evaluate a currently unknown attribute Y.
My first step was to let Y rate by some experts for a sample products (this cannot be done for all products due to the sheer quantity) and then predict Y for all the other products using a machine-learning predictive model fitted to the products that have been hand-rated.
However I have been thinking that I could equally understand this as a problem of missing data (i.e. Y is missing for all unrated items) and therefore use mechanisms like MICE to impute Y.
So is my train of thought valid, could I potentially use both predictive models (like RF, SVM, etc.) and imputation methods (like MICE, etc.) to fill in my unknown value Y for all products?
And if both is possible, how would I decide which method is more suited?