One of the basic challenges that we face when dealing with real-world data is overfitting versus underfitting your regressions to that data, or your models, or your predictions. When we talk about underfitting and overfitting, we can often talk about that in the context of bias and variance, and the bias-variance trade-off. So, let's talk about what that means.
So conceptually, bias and variance are pretty simple. Bias is just how far off you are from the correct values, that is, how good are your predictions overall in predicting the right overall value. If you take the mean of all your predictions, are they more or less on the right spot? Or are your errors all consistently skewed in one direction or another? If so, then your predictions are biased in a certain direction.
Variance is just a measure of how spread out, how scattered your predictions...
