Overfitting, Underfitting, Bias Variance Tradeoff, Bias, Variance.
MCQs are from the sample questions or are completely different, you’d have a low score either way. So if a model is said to be under-fitting, this means that the model learned no patterns from the training data and performs poorly on training data and test data. Why do we not want our models to