Intutition of why Bootstrap aggregating reduces overfitting? Can somebody give me a non-mathematical intuition why Bootstrap aggregating reduces overfitting?
From my point of view, we are not providing any additional information, we are not really enlengthen the observation number.

This is a phenomenon so called weak learners (see) in an ensemble decision yields a good performance. The reason of this explained by Dietterich here:

Uncorrelated errors made by the
individual classifiers can be
removed by voting.

Further explanation or theoretical justification of the statement could be an open research problem.
