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!!! Overview
[{$pagename}], in [Machine Learning], are the simplifying assumptions made by a [Machine Learning model] to make the target function easier to learn.
Generally parametric algorithms have a high [{$pagename}] making them fast to learn and easier to understand but generally less exible. In turn they are have lower predictive performance on complex problems that fail to meet the simplifying assumptions of the algorithms bias.
* Low [{$pagename}] suggests more assumptions about the form of the [Mapping function].
* High [{$pagename}] (referred to as [Underfitting]) Suggests less assumptions about the form of the [Mapping function].
Generally [nonparametric machine learning] [algorithms] that have a lot of flexibility have a high [Bias error].
Examples of low-[{$pagename}] machine learning [algorithms] include:
* Decision Trees
* k-Nearest Neighbors
* Support Vector Machines.
Examples of high-[{$pagename}] machine learning [algorithms] include:
* [Linear Regression]
* Linear Discriminant Analysis
* [Logistic Regression]
!! 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