Skip to main content
Artificial Intelligence

Return to Question

added 4 characters in body; edited title
Source Link
nbro
nbro
  • 41.4k
  • 12
  • 115
  • 205

Supervised Learning and Is the noise term $\epsilon$ in $y=g(x) + \epsilon$ used to denote the model's imperfection to the real world?

So, in supervidesIn supervised machine learning, it is common to say that we learn a function of the form $y=g(x) + \epsilon$.

$$y=g(x) + \epsilon.$$

Generally, $\epsilon$ is used to denote noise or, more preciseprecisely, any influence by latent variables such as measurement inaccuariesinaccuracies - right(right?).

Is it, therefore, correct to say that we use $\epsilon$ doto denote the model's imperfection to the real world (caused by anything unkownunknown)?

Thanks!

Supervised Learning and the noise term

So, in supervides machine learning it is common to say that we learn a function of the form $y=g(x) + \epsilon$. Generally, $\epsilon$ is used to denote noise or more precise any influence by latent variables such as measurement inaccuaries - right?

Is it therefore correct to say that we use $\epsilon$ do denote the model's imperfection to the real world (caused by anything unkown)?

Thanks!

Is the noise term $\epsilon$ in $y=g(x) + \epsilon$ used to denote the model's imperfection to the real world?

In supervised machine learning, it is common to say that we learn a function of the form

$$y=g(x) + \epsilon.$$

Generally, $\epsilon$ is used to denote noise or, more precisely, any influence by latent variables such as measurement inaccuracies (right?).

Is it, therefore, correct to say that we use $\epsilon$ to denote the model's imperfection to the real world (caused by anything unknown)?

Source Link
Micha Christ
Micha Christ
  • 1

Supervised Learning and the noise term

So, in supervides machine learning it is common to say that we learn a function of the form $y=g(x) + \epsilon$. Generally, $\epsilon$ is used to denote noise or more precise any influence by latent variables such as measurement inaccuaries - right?

Is it therefore correct to say that we use $\epsilon$ do denote the model's imperfection to the real world (caused by anything unkown)?

Thanks!