MACHINE LEARNING

Detailed explanation-1: -Regularization is the answer to overfitting. It is a technique that improves model accuracy as well as prevents the loss of important data due to underfitting. When a model fails to grasp an underlying data trend, it is considered to be underfitting.

Detailed explanation-2: -Overfitting occurs when the model cannot generalize and fits too closely to the training dataset instead. Overfitting happens due to several reasons, such as: The training data size is too small and does not contain enough data samples to accurately represent all possible input data values.

Detailed explanation-3: -L1 regularization. L1 regularization, also known as L1 norm or Lasso (in regression problems), combats overfitting by shrinking the parameters towards 0. This makes some features obsolete.

Detailed explanation-4: -You should use regularization if the gap in performance between train and test is big. This means the model grasps too much details of the train set. Overfitting is related to high variance, which means the model is sensitive to specific samples of the train set.