Bias is the difference between a model’s predicted values and the observed or the actual values. Variance of a model is the difference between predictions of the model when fit to the train and the test data set.
When a model is too simple, then it cannot make perfectly accurate predictions. However, the predictions will be consistent. In such a case, the model will be underfitting and have high bias and low variance. On the other hand, if the model is too complex, it can predict accurately but not consistently. In this case, the model is said to have high variance low bias indicating it will much better fit the train data than the test. Such a model is an overfitted model.
