MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -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-2: -Training data must be labeled-that is, enriched or annotated-to teach the machine how to recognize the outcomes your model is designed to detect. Unsupervised learning uses unlabeled data to find patterns in the data, such as inferences or clustering of data points.

Detailed explanation-3: -Unsupervised Learning areas of application include market basket analysis, semantic clustering, recommender systems, etc. The most commonly used Supervised Learning algorithms are decision tree, logistic regression, linear regression, support vector machine.

Detailed explanation-4: -Training data is the initial dataset you use to teach a machine learning application to recognize patterns or perform to your criteria, while testing or validation data is used to evaluate your model’s accuracy. You’ll need a new dataset to validate the model because it already “knows” the training data.

Detailed explanation-5: -Some popular examples of supervised machine learning algorithms are: Linear regression for regression problems. Random forest for classification and regression problems. Support vector machines for classification problems.