MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Common classification algorithms are linear classifiers, support vector machines (SVM), decision trees, k-nearest neighbor, and random forest, which are described in more detail below. Regression is used to understand the relationship between dependent and independent variables.

Detailed explanation-2: -A supervised machine learning model will learn to identify patterns and relationships within a labelled training dataset. Once the relationship between input data and expected output data is understood, new and unseen data can be processed by the model.

Detailed explanation-3: -Supervised learning is a machine learning approach that’s defined by its use of labeled datasets. These datasets are designed to train or “supervise” algorithms into classifying data or predicting outcomes accurately. Using labeled inputs and outputs, the model can measure its accuracy and learn over time.

Detailed explanation-4: -Supervised learning algorithms try to model relationships and dependencies between the target prediction output and the input features such that we can predict the output values for new data based on those relationships which it learned from the previous data sets.