In your RandomForest implementation, on the line in fit() where you're building the training subsets to give to each tree, it appears that your bagging approach doesn't use 'sampling with replacement' strategy.

    idx = np.random.choice(range(n_features), size=self.max_features, replace=False)   

It would appear that the replace=False prevents the 'sampling with replacement' behavior usually implemented by bagging algorithms. Should the replace=False be changed to replace=True?