Given the true model is
Y=f(X) + e
so expected squared error at a point x is
Bias shows how different between the average prediction and the true value
Variance show the everage between the prediction values and average of prediction value
High bias means underfitting. The intuition is that it learns a wrong set
High variance means overfitting. The intuition behide this is that it learns every sample by heart and lack of generalization
Everything yours 