MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Overfitting is a modeling error in statistics that occurs when a function is too closely aligned to a limited set of data points. As a result, the model is useful in reference only to its initial data set, and not to any other data sets.

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: -Underfitting means that your model makes accurate, but initially incorrect predictions. In this case, train error is large and val/test error is large too. Overfitting means that your model makes not accurate predictions. In this case, train error is very small and val/test error is large.

Detailed explanation-4: -Overfitting is a concept in data science, which occurs when a statistical model fits exactly against its training data. When this happens, the algorithm unfortunately cannot perform accurately against unseen data, defeating its purpose.

Detailed explanation-5: -Overfitting is also known as the low bias and high variance problem and Underfitting is known as the high bias and low variance problem. High bias means when the training error is much greater than the base error and high variance shows that the validation error is much higher compared to the training error.