MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -The main difference between supervised and unsupervised learning: Labeled data. The main distinction between the two approaches is the use of labeled datasets. To put it simply, supervised learning uses labeled input and output data, while an unsupervised learning algorithm does not.

Detailed explanation-2: -Supervised learning uses labeled datasets, whereas unsupervised learning uses unlabeled datasets. By “labeled” we mean that the data is already tagged with the right answer. A classification problem uses algorithms to classify data into particular segments.

Detailed explanation-3: -What is unsupervised learning? Unsupervised learning, also known as unsupervised machine learning, uses machine learning algorithms to analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention.

Detailed explanation-4: -Unsupervised methods usually assign data points to clusters, which could be considered algorithmically generated labels. We don’t “learn” labels in the sense that there is some true target label we want to identify, but rather create labels and assign them to the data.

Detailed explanation-5: -most of the time, data is unlabeled, unstructured, and chaotic. Unfortunately, most of the machine learning algorithms that are widely used today depend heavily on labeled data and fully supervised algorithms.