MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -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-2: -If a model has been trained too well on training data, it will be unable to generalize. It will make inaccurate predictions when given new data, making the model useless even though it is able to make accurate predictions for the training data. This is called overfitting.

Detailed explanation-3: -Generalization is low if there is large gap between training and validation loss. Regularization. Regularization is a method to avoid high variance and overfitting as well as to increase generalization.

Detailed explanation-4: -Finally, you learned about the terminology of generalization in machine learning of overfitting and underfitting: Overfitting: Good performance on the training data, poor generliazation to other data. Underfitting: Poor performance on the training data and poor generalization to other data.

Detailed explanation-5: -When the model memorizes the noise and fits too closely to the training set, the model becomes “overfitted, ” and it is unable to generalize well to new data. If a model cannot generalize well to new data, then it will not be able to perform the classification or prediction tasks that it was intended for.