Bias error is used to measure how much on an average the predicted values vary from the actual values. In case a high-bias error occurs, we have an under-performing model.
Variance is used to measure how the predictions made on the same observation differ from each other. A high-variance model will overfit the dataset and perform badly on any observation.
