6 Questions I was Asked at Data Scientist Interviews

In machine learning, overfitting arises when a model tries to fit the training data so well that it cannot generalize to new observations. Thus, overfit models seem to be outstanding on training data but performs poor on new, previously unseen observations. The main reason of overfitting is model complexity. Regularization controls the model complexity by penalizing higher terms in the model. If a regularization terms is added, the model tries to minimize both loss and complexity of model.