If Occam’s Razor suggests that more complex models don’t generalize well, then in applied machine learning, it suggests we should choose simpler models as they will have lower prediction errors on new data. This has implications in machine learning, as we are specifically trying to generalize to new unseen cases from specific observations, referred to as inductive reasoning.
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