Machine Learning, Data Science, Overfitting, Underfitting.
wrong and too simple — for example, your data is quadratic and your model is linear. This situation is also called high bias. This means that your algorithm can make accurate predictions, but the initial assumption about the data is incorrect. Underfitting. The linear model trained on cubic data.