In bias variance trade off graph
Bias is the difference between actual and predicted value in training data set so train error (dotted red curve) and bias(red curve ) looks same
Variance is the difference between actual and predicted value in test data set i was expecting the test error (dotted blue curve) and variance (blue curve) to be same but they are not same ? Please help to understand this part
Analysis done : 1.Model poorly fits data set then we have high bias and high training error this looks reasonable but it also has low variance with high test error
if a model is not able to capture the pattern in the dataset it would not be able to predict correct value on which it was trained on( train set ) so high training error = high bias
so it will also not be able to predict the correct value for test set right then why is that all graph in internet show low variance for low complex model in bias variance trad off graph high test error == low variance ? ? why high test error =! high variance ?