if we move on to the dartboard in the upper right cornerwe see that our points are all consistently skewed from where they should beto the northwest so this is an example of high bias in our predictionswhere they're consistently off by certain amount we have low variance because they're all clustered tightly around the wrong spotbut at least they're close togetherso we're being consistent in our predictions that' low variance butthe bias is high so againthis is high biaslow variance in the dartboard in the lower left corneryou can see that our predictions are scattered around the wrong mean point sowe have high biaseverything is skewed to some place where it shouldn' be but our variance is also high sothis is kind of the worst of both worlds herewe have high bias and high variance in this example finallyin wonderful perfect worldyou would have an example like the lower right dartboardwhere we have low biaswhere everything is centered around where it should beand low variancewhere things are all clustered pretty tightly around where they should be soin perfect world that' what you end up with in realityyou often need to choose between bias and variance it comes down to over fitting vs underfitting your data let' take look at the following example it' little bit of different way of thinking of bias and variance soin the left graphwe have straight lineand you can think of that as having very low variancerelative to these observations sothere' not lot of variance in this linethat isthere is low variance but the biasthe error from each individual pointis actually high