why test error and variance has different curve in bias variance trade off graph? 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 ?



Variance is the difference between actual and predicted value in test
data set

No it's not. It's the sensitivity of the model to the small changes in data. Changes happen in the test data because the model already knows about the training data. So, high variance means high test error.
If the model has high bias, both training and test performance will be poor. For example, a model that always outputs a constant prediction has high bias (in may problems), and low variance, zero variance to be precise. One wouldn't expect low test error in this case.
