Intuitively, we wish to reduce the uncertainty of the model's predictions, while also improving the fit between the model's predictions and the known labels.
let us focus now on the uncertainty of the model's predictions:  by decreasing the uncertainty of the prediction distributions of individual features and simultaneously increasing the agreement among features (that is, decreasing their pairwise divergence), we decrease an upper bound on H(π x ).