![]() ![]() 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. Complex Hypothesis: More assumptions, and in turn, narrow applicability.Simple Hypothesis: Fewer assumptions, and in turn, broad applicability.Conversely, fewer assumptions suggests a more general hypothesis with greater predictive power to more cases. That is, the more assumptions a hypothesis has, the more narrow it is expected to be in its application. They may include details of specific cases that are at hand or easily available, and in turn, may not generalize to new cases. The problem with complex hypotheses with more assumptions is that they are likely too specific. This is known as Occam’s Razor after the medieval philosopher William of Occam (or Ockham). There is a long-standing tradition in science that, other things being equal, simple theories are preferable to complex ones. It is not a rule, more of a heuristic for problem-solving, and is commonly invoked in science to prefer simpler hypotheses that make fewer assumptions over more complex hypotheses that make more assumptions. Occam’s Two Razors: The Sharp and the Blunt, 1998. William of Occam’s famous razor states that “Nunquam ponenda est pluralitas sin necesitate,” which, approximately translated, means “Entities should not be multiplied beyond necessity”. It is named for William of Ockham and was proposed to counter ever more elaborate philosophy without equivalent increases in predictive power. Occam’s Razor: If all else is equal, the simplest solution is correct.Occam’s Razor is an approach to problem-solving and is commonly invoked to mean that if all else is equal, we should prefer the simpler solutions. , Data Mining: Practical Machine Learning Tools and Techniques, 2016. The idea is that the best scientific theory is the smallest one that explains all the facts. The rationale for choosing simpler models is tied back to Occam’s Razor. Simpler models are typically defined as models that make fewer assumptions or have fewer elements, most commonly characterized as fewer coefficients (e.g. choose the model with the highest accuracy or lowest prediction error.Īnother important consideration is to choose simpler models over complex models. It is often straightforward to select a model based on its expected performance, e.g. Model selection is the process of choosing one from among possibly many candidate machine learning models for a predictive modeling project. Occam’s Two Razors for Machine Learning.This tutorial is divided into three parts they are: Photo by dylan_odonnell, some rights reserved. Kick-start your project with my new book Ensemble Learning Algorithms With Python, including step-by-step tutorials and the Python source code files for all examples.Įnsemble Learning Algorithm Complexity and Occam’s Razor ![]() Ensemble learning algorithms like boosting provide a specific case of how the second razor fails and added complexity can result in lower generalization error.The heuristic can be divided into two razors, one of which is true and remains a useful tool and the other that is false and should be abandoned.Occam’s razor is a heuristic that suggests choosing simpler machine learning models as they are expected to generalize better.In this tutorial, you will discover how to reconcile Occam’s Razor with ensemble machine learning.Īfter completing this tutorial, you will know: These findings are at odds with the Occam’s razor principle taken at face value. Further, empirical results show a continued reduction in generalization error as the complexity of an ensemble learning model is incrementally increased. In practice, ensembles are almost universally the type of model chosen on projects where predictive skill is the most important consideration. In machine learning, it suggests complex models like ensembles will overfit the training dataset and perform poorly on new data. 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. Occam’s razor suggests that in machine learning, we should prefer simpler models with fewer coefficients over complex models like ensembles. ![]()
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