I was wondering if somebody could explain something to me.
I am working on the deep learning fundamentals course of the data science path.
In most of the assignments, we create a dataset using the sklearn library by using for example.
make_regression(), Make_moons(), etc.
data = make_moons(100, random_state=3, noise=0.04)
features = pd.DataFrame(data)
labels = pd.Series(data)
features[“bias”] = 1
However, we always create an additional column called ‘bias’. Is the reason for this because the features created by classes would otherwise explain all the variation in target column? Or is there another reason?
Thank you in advance.