models with high biashigh variance are the worst sort of models possibleas they will not learn necessary data attribute relationships that are essential to correlation with output responses also they will be extremely sensitive to data and outliers and noise leading to highly fluctuating predictions which result in high varianceas depicted in the bottom-right image extreme cases of bias-variance in real-world modelingwe will always have tradeoff between decreasing bias and variance simultaneously to understand why we have this tradeoffwe must first consider the two possible extreme cases of bias and variance underfitting consider linear model that is lazy and always predicts constant value this model will have extremely low variance (in fact it will be zero variance modelas the model is not dependent at all on which subset of data it gets it will always predict constant and hence have stable performance but on the other hand it will have extremely high bias as it has not learned anything from the data and made very rigid and erroneous assumption about the data this is the case of model underfittingin which we fail to learn anything about the dataits underlying patternsand relationships  verfitting consider the opposite case in which we have model that attempts to fit every data point it encounters (the closest example would be fitting an th order polynomial curve for an -observation dataset so that the curve passes through each pointin this casewe will get model which will have low bias as no assumption to structure of data was made (even when there was some structurebut the variance will be very high as we have tightly fit the model to one of the possible subsets of data (focusing too much on the training dataany subset different from the training set will lead to lot of error this is the case of overfittingwhere we have built our model so specific to the data at hand that it fails to do any generalization over other subsets of data the tradeoff the total generalization error of any model will be sum of its bias errorvariance errorand irreducible erroras depicted in the following equation generalization error bias error variance error irreducible error such that the irreducible error is the error that gets introduced due to noise in the training data itselfsomething that is common in real-world datasets and not much can be done about it the idea is to focus on the other two errors every model needs to do tradeoff between the two choicesmaking assumptions about the structure of data or fitting itself too closely to the data at hand either choice in entirety will lead to one of the extreme cases the idea is to focus on balancing model complexity by doing an optimal tradeoff between bias and varianceas depicted in figure -