bias/variance trade-off one of the basic challenges that we face when dealing with real-world data is overfitting versus underfitting your regressions to that dataor your modelsor your predictions when we talk about underfitting and overfittingwe can often talk about that in the context of bias and varianceand the bias-variance trade-off solet' talk about what that means so conceptuallybias and variance are pretty simple bias is just how far off you are from the correct valuesthat ishow good are your predictions overall in predicting the right overall value if you take the mean of all your predictionsare they more or less on the right spotor are your errors all consistently skewed in one direction or anotherif sothen your predictions are biased in certain direction variance is just measure of how spread outhow scattered your predictions are soif your predictions are all over the placethen that' high variance butif they're very tightly focused on what the correct values areor even an incorrect value in the case of high biasthen your variance is small let' look at some examples let' imagine that the following dartboard represents bunch of predictions we're making where the real value we're trying to predict is in the center of the bullseyestarting with the dartboard in the upper left-hand corneryou can see that our points are all scattered about the center so overallyou know the mean error comes out to be pretty close to reality our bias is actually very lowbecause our predictions are all around the same correct point howeverwe have very high variancebecause these points are scattered about all over the place sothis is an example of low bias and high variance