Bias in Linear Regression (x-intercept) vs Statistics

[WWGD]

TL;DR

Trying to Reconcile two apparently/superficially different usages of the tern "Bias"

Hi,
In simple regression for machine learning , a model :

Y=mx +b ,

Is said AFAIK, to have bias equal to b. Is there a relation between the use of bias here and the use of bias in terms of estimators

for population parameters, i.e., the bias of an estimator P^ for a population parameter P is defined as the difference E[P^]- P?

The two do not seem to coincide as Y^= mx^ +b^ is an unbiased estimator of the population parameter Y . Can anyone explain the

disparity?

 

[Dale]

Words have more than one meaning. I have never seen bias used with the first meaning, so that appears to be a specialized field of study just “hijacking” terminology from other fields of study. It happens often. I am afraid there is not much justification needed or provided for that type of thing.

 

[FactChecker]

I think that the two uses are only logically similar in the context of a model where X and Y are known or assumed to be proportional (Y = mx). In that case, b would be a bias due to something.