Hello everyone,

I don’t understand why a high-bias model leads to a low-variance model. Can anyone explain what are the differences between bias and variance of a model?

Thank you in advance!

Hello everyone,

I don’t understand why a high-bias model leads to a low-variance model. Can anyone explain what are the differences between bias and variance of a model?

Thank you in advance!

Please provide a link to the lesson/course that you’re referring to.

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Hello,

Trying to understand better these concepts (because I’m attending the Machine learning courses on the platform), I found this page where the following paragraph it’s mentioned:

‘‘Bias and variance are closely related in machine learning. If a high-bias model is created, it leads to a low-variance model due to the lack of ability to accurately represent the data. On the other hand, if a low-bias model is created, it leads to a high-variance model due to the ability of the algorithm to accurately represent the data.’’

Thank you!

This is actually a pretty accurate reference: Bias–variance tradeoff - Wikipedia.

Note that it’s possible to have high variance and high bias so context is really important.

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