MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Cross-validation is primarily used in applied machine learning to estimate the skill of a machine learning model on unseen data. That is, to use a limited sample in order to estimate how the model is expected to perform in general when used to make predictions on data not used during the training of the model.

Detailed explanation-2: -Cross-validation is a technique for evaluating ML models by training several ML models on subsets of the available input data and evaluating them on the complementary subset of the data. Use cross-validation to detect overfitting, ie, failing to generalize a pattern.

Detailed explanation-3: -Explanation: Cross-validation is also used to pick type of prediction function to be used. 2. Point out the wrong combination. a) True negative=correctly rejected. b) False negative=correctly rejected.

Detailed explanation-4: -What is the purpose of performing cross-validation? C. Cross-validation is a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set.

Detailed explanation-5: -The goal of cross-validation is to test the model’s ability to predict new data that was not used in estimating it, in order to flag problems like overfitting or selection bias and to give an insight on how the model will generalize to an independent dataset (i.e., an unknown dataset, for instance from a real problem).