MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Explanation: The problem of unsupervised learning involves learning patterns in the input when no specific output values are supplied. We cannot expect the specific output to test your result. Here the agent does not know what to do, as he is not aware of the fact what propose system will come out.

Detailed explanation-2: -Explanation: In unsupervised learning, the model learns itself from the data without having a predicted result. Either the data is not given with a target response variable (label), or none chooses to label a response.

Detailed explanation-3: -Unsupervised algorithm only processes “features” and does not process tags. Dimensionality reduction algorithm is not unsupervised learning. K-means algorithm and SVM algorithm belong to unsupervised learning. None of the above 2.

Detailed explanation-4: -Unsupervised learning algorithms are used to group cases based on similar attributes, or naturally occurring trends, patterns, or relationships in the data. These models also are referred to as self-organizing maps. Unsupervised models include clustering techniques and self-organizing maps.

Detailed explanation-5: -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.