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Occam’s razor

What is Occam’s razor?

Occam’s razor (which is also spelled ‘Ockham’s razor’) is also referred to as the Law of Economy or the Law of Parsimony. It is a problem-solving principle that has been attributed to the 14th-century logician, friar, and scholastic philosopher William of Ockham.

William of Ockham states that “pluralitas non est ponenda sine necessitate”, or “plurality should not be posited without necessity”.

Essentially, the Law of Economy says that “Entities are not to be multiplied beyond necessity.”

This principle of philosophical reasoning advocates simplicity. 

It suggests that among two rival theories, the one with the fewest assumptions should be selected.

This is because the simplest theory will be much easier to verify and the simpler the solution, the less difficulty will be involved in executing and implementing it.

It is used widely in science. Even Isaac Newton, in Principia Mathematica, said that "We are to admit no more causes of natural things than such as are both true and sufficient to explain their appearances."

Basically, account for the observed facts, but keep your theories as simple as possible.

How is Occam's razor applied in machine learning?

Some ML experts believe that Occam’s razor is helpful in designing machine learning models. They say that it helps in deciding which algorithms to use in the machine learning project and also in figuring out how to train the model with that algorithm.

When multiple suitable algorithms have comparable trade-offs, they opt for the one that is the simplest to deploy and easiest to interpret. 

Simplifying models by using dimensionality reduction and feature selection are considered to be examples of Occam’s razor in action in the sphere of machine learning.

Occam’s razor can also be cited regarding the problem of overfitting which occurs when models are far too complex to fit the data that is being analyzed and processed. An extremely complex model might be more fragile.


Is Occam’s razor always true?

Simplicity helps you clearly see what is most important in a phenomenon. Among two competing hypotheses, the simpler one might not always be the better option. Sometimes, it could be far less accurate.

Occam’s razor can only really be used if both the theories available predict identical results, and one of the theories makes fewer assumptions than the other one (is simpler than the other available hypothesis).

Science doesn’t tend to be that simple. It tends to be complex and messy. Using Occam’s razor can be tempting, but it might not always be the best way for you to proceed.

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Occam’s razor

October 14, 2020

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What is Occam’s razor?

Occam’s razor (which is also spelled ‘Ockham’s razor’) is also referred to as the Law of Economy or the Law of Parsimony. It is a problem-solving principle that has been attributed to the 14th-century logician, friar, and scholastic philosopher William of Ockham.

William of Ockham states that “pluralitas non est ponenda sine necessitate”, or “plurality should not be posited without necessity”.

Essentially, the Law of Economy says that “Entities are not to be multiplied beyond necessity.”

This principle of philosophical reasoning advocates simplicity. 

It suggests that among two rival theories, the one with the fewest assumptions should be selected.

This is because the simplest theory will be much easier to verify and the simpler the solution, the less difficulty will be involved in executing and implementing it.

It is used widely in science. Even Isaac Newton, in Principia Mathematica, said that "We are to admit no more causes of natural things than such as are both true and sufficient to explain their appearances."

Basically, account for the observed facts, but keep your theories as simple as possible.

How is Occam's razor applied in machine learning?

Some ML experts believe that Occam’s razor is helpful in designing machine learning models. They say that it helps in deciding which algorithms to use in the machine learning project and also in figuring out how to train the model with that algorithm.

When multiple suitable algorithms have comparable trade-offs, they opt for the one that is the simplest to deploy and easiest to interpret. 

Simplifying models by using dimensionality reduction and feature selection are considered to be examples of Occam’s razor in action in the sphere of machine learning.

Occam’s razor can also be cited regarding the problem of overfitting which occurs when models are far too complex to fit the data that is being analyzed and processed. An extremely complex model might be more fragile.


Is Occam’s razor always true?

Simplicity helps you clearly see what is most important in a phenomenon. Among two competing hypotheses, the simpler one might not always be the better option. Sometimes, it could be far less accurate.

Occam’s razor can only really be used if both the theories available predict identical results, and one of the theories makes fewer assumptions than the other one (is simpler than the other available hypothesis).

Science doesn’t tend to be that simple. It tends to be complex and messy. Using Occam’s razor can be tempting, but it might not always be the best way for you to proceed.

Thanks for reading! We hope you found this helpful.

Ready to level-up your business? Click here.

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