# Handling missing attributes when using weighted average

I was trying this optimization where I have some predictors and I want to predict the real value using weighted average of the predictors. What I am trying to do is given the predictors' prediction and the ground truth values, I will calculate the weighted average and the weighted average has weights defined k1,k2,...kN for the N predictors such that when I take the weighted average the variance of the error of the weighted average is minimized.

Now at times, the predictors' predictions are missing. How can I handle these missing values?

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