Ensemble Learning Algorithm Complexity and Occam’s Razor

Occam’s razor suggests that in machine learning, we should prefer simpler models with fewer coefficients over complex models like ensembles.

Taken at face value, the razor is a heuristic that suggests more complex hypotheses make more assumptions that, in turn, will make them too narrow and not generalize well. In machine learning, it suggests complex models like ensembles will overfit the training dataset and perform poorly on new data.

In practice, ensembles are almost universally the type of model chosen on projects where predictive skill is the most important consideration. Further, empirical results show a continued reduction in generalization error as the complexity of an ensemble learning model is incrementally increased. These findings are at odds with the Occam’s



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