What is the difference between under fitting and over fitting in machine learning? Underfitting occurs: a) When a model is too simple b) Fails to capture the underlying pattern in data c) Performs poorly on both training and test data d) Characterized by high bias and low variance Typical causes: 1) Too few features 2) Oversimplified model 3) Insufficient training Overfitting occurs: a) when a model is too complex b) Learns noise and random fluctuations in the data c) Performs very well on train