I am learning about Bias-Variance for classification. It is mentioned that Bias is the distance of main prediction (mean for regression, mode for classification) and variance is the average disagreement of main prediction from each prediction.
What is meant by average prediction here? Does that mean that we learn a classifier on particular sample from population and then calculate predictions for other samples from same population?
