What are the meanings of Bias and Under fitting which are used in machine learning contexts? I have faced biased data in sampling in Statics but it seems they are different things in learning concepts.I have heard that some data sets are biased, also have heard the model (for example neural networkd) is low bias. Are these bias different?
What is Bias and Under fitting?
machine learning
Bias can mean different things in statistics:If your model is biased, it's likely your model is under-fitting.Some data set is biased in sample collection. For instance, if you assume your sample responses are independent, but somehow it's not, this is a bias in your data set. If you want to sample everybody in the country, but you skip some cities for no reason, this causes bias in your data set.Your estimators could be biased - the expectation of your estimator is not equal to the true value in the population.