- #1

- 6,723

- 9,669

- TL;DR Summary
- 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?

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?