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1) Changing the phrasing "algorithms suitable for nonparametric regression problems." It does not make sense to describe a *problem* as nonparametric. It is a *model* that can be classified as either parametric or non-parametric. A regression problem can be solved using either parametric or non-parametric models. 2) I'm removing neural networks and support vector machines from the list of non-parametric models because they are not nonparametric. |
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</math>
where the random variable <math>U</math> is the `noise term', with mean 0.
Without the assumption that <math>m</math> belongs to a specific parametric family of functions it is impossible to get an unbiased estimate for <math>m</math>, however most estimators are [[consistent|Consistency_(statistics)]] under suitable conditions.
== List of general-purpose nonparametric regression algorithms ==
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