Is Confidence based on Tree Variance as good a metric for RandomForestClassifier as it is for RandomForestRegressor?

I got this doubt while working on the RF Interpretation for a classification problem. The code @jeremy used in the lesson for this metric for the Bulldozers’ Regressor was this:

%time preds = np.stack([t.predict(X_valid) for t in m.estimators_])
np.mean(preds[:,0]), np.std(preds[:,0])

So what we’re doing there is to see how each tree varies from the mean of the predictions of all trees. But is the mean of the trees’ predictions as reliable for Classification as it is for Regression in the first place?