Machine learning models are not perfect; they are prone to a number of errors. The two most common sources of errors are bias and variance. Although two distinct problems, they are interconnected and relate to a model's available degree of freedom or complexity.
Bias, variance, and the trade-off
What is bias?
Bias refers to the inability of a method to correctly estimate the target. This does not only apply to machine learning. For example, in statistics, if we want to measure a population's average and do not sample carefully, the estimated average will be biased. In simple terms, the method's (sampling) estimation will not closely match the actual target (average).
In machine learning, bias refers to the difference...
