figure - the bias-variance tradeoff in figure - the inner red circle represents the perfect model that we can have considering all the combinations of the data that we can get each blue dot (marks model that we have learned on the basis of combinations of the dataset and features that we get models with low biaslow variancerepresented by the top left imagewill learn good general structure of underlying data patterns and relationships that will be close to the hypothetical model and predictions will be consistent and hit the bull' eyemodels with low biashigh variancerepresented by the top right imageare models that generalize to some extent (learn proper relationships\patternsand perform decently on average due to low bias but are sensitive to the data it is trained on leading to high variance and hence predictions keep fluctuating models with high biaslow variance will tend to make consistent predictions irrespective of datasets on which the models are built leading to low variance but due to high biasit will not learn the necessary patterns\relationships in the data that are required for correct predictions and hence misses the mark due to the high bias error on averageas depicted in the bottom-left image