Check all the correct statements about Double Descent $($DD$)$

Group of answer choices

DD can be easily checked on a toy datasets

DD is an improved version of Gradient Descent when we start to decrease the loss function after some number of iterations

DD only valid when both amount for training data and model weights are huge

DD is fairly new concept while bias$-$variance trade$-$off is a classical one

DD extends bias$-$variance trade$-$off curve to the area of an enormous complexity

The extremum point for DD is somewhere the number of weights approximately equals to the amount of training data