MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -A classification algorithm is a supervised learning technique that uses data training to determine data into different classes. Classification predictive modeling is trained using data or observations, and new observations are categorized into classes or groups.

Detailed explanation-2: -Characteristics of a Good Classification That is, classification should be absolutely clear. Homogeneity: The items within a specific group or class should be similar to each other. Suitability: The attribute or characteristic according to which classification is done should agree with the purpose of classification.

Detailed explanation-3: -Supervised learning uses a training set to teach models to yield the desired output. This training dataset includes inputs and correct outputs, which allow the model to learn over time. The algorithm measures its accuracy through the loss function, adjusting until the error has been sufficiently minimized.

Detailed explanation-4: -Classification is a supervised machine learning method where the model tries to predict the correct label of a given input data. In classification, the model is fully trained using the training data, and then it is evaluated on test data before being used to perform prediction on new unseen data.

Detailed explanation-5: -The Classification algorithm is a Supervised Learning technique that is used to identify the category of new observations on the basis of training data. In Classification, a program learns from the given dataset or observations and then classifies new observation into a number of classes or groups.