As such, any assumptions you add to your theory introduce further possibilities for error, and if an assumption isn’t improving the accuracy of a theory, it just increases the probability the theory is wrong. All things can be ascribed a probability of happening. You can think of it in terms of basic probability theory. As medical students are sometimes told, “When you hear hoof beats, think horses, not zebras.” Or as the US Navy KISS design principle states,“Keep it simple, stupid.” Or if you are a doctor and a patient turns up complaining of a blocked nose, it is more likely they have a common cold than a rare immune-system disorder. He is probably best known today for his espousal of metaphysical nominalism indeed, the methodological principle known as Ockham’s Razor is named after him. If two computer programs do the same job, for example, the shorter one, in which less code can go wrong, is probably preferable. 12871347) is, along with Thomas Aquinas and John Duns Scotus, among the most prominent figures in the history of philosophy during the High Middle Ages. The principle can be applied in many fields of science and logic. Many other people before and after the friar, including Albert Einstein and Isaac Newton, have come up with similar rules, but it is generally attributed (via an alternative spelling of the name of the village in which he grew up) to Ockham because he used the principle with such razor-like logic to state, along with other things, that “God’s existence cannot be deduced by reason alone.” It all has to do with examining the particular project scope and what must be obtained, and looking at the inputs, the training sets and the parameters to apply the most targeted solutions for the given result.Occam’s razor is a principle often attributed to 14 th century friar William of Ockham that says that if you have two competing ideas to explain the same phenomenon, you should prefer the simpler one. In some cases, complexity can be necessary and beneficial. On the other hand, some point out that using Occam's razor incorrectly can reduce the effectiveness of machine learning programming. This is another example where someone might cite Occam's razor in the deliberate design of machine learning systems, to make sure that they don't suffer from overcomplexity and rigidity. There is a problem called overfitting where models are made too complex to really fit the data being examined and the use case for that data. Yet another popular application of Occam's razor to machine learning involves the “curse of overly complex systems.” This argument goes that creating a more intricate and detailed model can make that model fragile and unwieldy. Another argument goes that when creative people brainstorm how to assess the business use case and limit the scope of a project before using algorithms, they're using Occam's razor to whittle down the complexity of the project from the very beginning. In limiting the sets of parameters for a project, engineers could be said to be “using Occam's razor” to simplify the model. On the other hand, others describe model trade-offs where engineers reduce complexity at the expense of accuracy – but still argue that this Occam's razor approach can be beneficial.Īnother application of Occam's razor involves the parameters set for certain kinds of machine learning, such as Bayesian logic in technologies. Others point out that simplification procedures such as feature selection and dimensionality reduction are also examples of using an Occam's razor principle – of simplifying models to get better results. One interpretation of Occam's razor is that, given more than one suitable algorithm with comparable trade-offs, the one that is least complex to deploy and easiest to interpret should be used. Some contend that Occam's razor can help engineers to choose the best algorithm to apply to a project, and also help with deciding how to train a program with the selected algorithm. With that in mind, some experts feel that Occam's razor can be useful and instructive in designing machine learning projects. Machine learning involves implementing algorithms, data structures and training systems to computers, to allow them to learn on their own and produce evolving results. So, you send out an email to him and you don’t hear a response. You’re having a busy Monday and you want a coworker’s assistance on a task. With machine learning, engineers work to train computers on sets of training data, to enable them to learn and go beyond the limits of their original codebase programming. Examples of the Occam’s Razor: Occam’s razor applies in various fields due to the generic guideline it provides. However, Occam's razor also has some modern applications to state-of-the-art technologies – one example is the application of the principle to machine learning. The use of Occam's razor dates back to William of Ockham in the 1200s – it's the idea that the simplest and most direct solution should be preferred, or that with different hypotheses, the simplest one or the one with fewest assumptions will be best applied.
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