Is it true that lots of bias is equivalent to underfitting, while lots of variance is equivalent to overfitting?

From what I understand, there is a relationship between bias and underfitting; as well as variance and overfitting.

Is a 'biased model' another word for an 'underfitted model'? Likewise, is a "varianced' model" another word for an 'overfitted model'?

• I understand what you are trying to say, but I don't think "varianced" is a word, even in statistics. ;-) Commented Jun 21, 2014 at 2:28
• Hahahahaha, yeah I wasn't too sure
– user46925
Commented Jun 21, 2014 at 2:35
• I've seen high variance models described as unstable (or more specifically the model estimates are unstable). Commented Jun 21, 2014 at 4:35
• Think about the cluster of arrows in target practice. The centroid of the cluster is the "mean" while its diameter is related to its variance. If the mean is close to the center of the target then your archer is accurate. If the standard deviation is small then the archer is precise. ... so there is a joke about two statisticians hunting, one had an arrow go 1 meter to the left, the other one meter to the right and they high-fived because they averaged out to a successful hit. ... bias is about "expected" distance from target to fit while variance is variance of that distance. Commented Jun 21, 2014 at 5:16