Overview#

Bias error, 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 Bias error 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 Bias error suggests more assumptions about the form of the Mapping function.
• High Bias error (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-Bias error machine learning algorithms include:

• Decision Trees
• k-Nearest Neighbors
• Support Vector Machines.

Examples of high-Bias error 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:

• Bias error
• Machine Learning model
• Overfitting
• Supervised Learning
• Underfitting