MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Supervised: All data is labeled and the algorithms learn to predict the output from the input data. Unsupervised: All data is unlabeled and the algorithms learn to inherent structure from the input data.

Detailed explanation-2: -Supervised learning, also known as supervised machine learning, is a subcategory of machine learning and artificial intelligence. It is defined by its use of labeled datasets to train algorithms that to classify data or predict outcomes accurately.

Detailed explanation-3: -Semi-supervised machine learning algorithms. Semi-supervised learning teaches an algorithm through a mix of labeled and unlabeled data. This algorithm learns certain information through a set of labeled categories, suggestions and examples.

Detailed explanation-4: -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-5: -Types of supervised Machine learning Algorithms: Linear Regression. Regression Trees. Non-Linear Regression. Bayesian Linear Regression.