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Variance error
Variance error

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!!! Overview
[{$pagename}] is the amount that the estimate of the [Mapping function] will change if different [Training dataset] was used.
The [Mapping function] is estimated from the [Training dataset] by some machine learning algorithm, so we should expect the [algorithm] to have some variance. Ideally, it should not change too much from one [Training dataset] to the next, meaning that the [algorithm] is good at picking out the hidden underlying mapping between the inputs and the output variables.
[Machine Learning] [algorithms] that have a high [[{$pagename}] are strongly in uenced by the specifics of the [Training dataset]. This means that the specifics of the training have in uences the number and types of parameters used to characterize the mapping function. ˆ
* Low [{$pagename}] - Suggests small changes to the estimate of the [Mapping function] with changes to the [Training dataset] ˆ
* High [{$pagename}] - Suggests large changes to the estimate of the [Mapping function] with changes to the [Training dataset] (Referred to a [Overfitting])
Examples of low-[{$pagename}] [Machine Learning] [algorithms] include:
* Linear Regression
* Linear Discriminant Analysis
* [Logistic Regression]
Examples of high-[{$pagename}] [Machine Learning] [algorithms] include:
* Decision Trees
* k-Nearest Neighbors
* Support Vector Machines.
!! More Information
There might be more information for this subject on one of the following:
[{ReferringPagesPlugin before='*' after='\n' }]
This page (revision-1) was last changed on 29-Nov-2024 16:16 by UnknownAuthorTop