Model Diagnostics, Machine Learning.
to overly simplistic assumptions in the learning algorithm. High bias can cause the model to miss relevant relations between features and target outputs. Variance: Error due to too much complexity in the learning algorithm. High variance can cause the model to model the random noise in the training