# Estimate values of missing features. Output label and trained model given

This is my first post at Cross Validated. I was having a doubt and hoped it can be cleared instead of stackoverflow.

Currently I'm working on a Predictive Model, which takes in server performance and classifies them weather there is an error in server or not. (Using RandomForest. Planning to change to NN).

My data is timeseries. Let us say our model predicts an error at t1 hours. When error is predicted, I take our feature score for that predictor. The top features are largest influencer for prediction. So, when error is predicted since I've to send out alert to support teams, I needed some more information for them to work on.

I was thinking of implementing something like this: Lets say when error is predicted, (Label = 1), we take out top 3 features (x, y, z). We then make it blank(consider missing). I've a trained RF model which was used to make predictions. Is it possible in any way to calculate expected values of x,y,z from trained model, if Label was specified?

It is something like backpropogation. Estimating values of missing features (x,y,z) if Label is given(=0. i.e. expected value for no error) and some features are given? (n:total features. Given : n-3 features).

I was thinking of implementing Regression separately on each feature, but that seems like tedious work, plus every feature fitting into regressor isnt a guarantee.

It is something like this following equation in lay man words.: eqn : 2a+3b+c+9x+10y+30z^2 = output. Given : a, b, c, output. Given : Trained model Calculate : x, y, z.

Equation, of algorithms, or anything to guide me in right way will be really helpful. I'm working on python. Any help will be greatly appreciated. Thanks in advance,

PS : If any doubt, or any issues with question, kindly post it in comments, so that I can edit the question. This is my 1st post here. Thanks.