Used to define the test sample to identify its class.

Used to define the test sample to identify its class.

• A. Regression
• B. Classification
• C. Unsupervised Learning
• D. Anomaly Detection

Answer: (B)

Assigning a test instance to a predefined category based on its features is asking you to perform classification. This is a supervised learning task where the model is trained on labeled examples so it learns how the input features map to discrete classes. When a new sample is tested, the model predicts which class it belongs to, producing a categorical outcome. This differs from regression, which aims to predict a continuous value rather than a category. Unsupervised learning doesn’t use labeled classes to map inputs; it discovers structure or groups in the data, such as clusters, rather than assigning a specific class to each new instance. Anomaly detection focuses on identifying unusual or outlying instances, not routinely labeling every sample with a standard class, though some approaches frame it as a special one-class classification task.

Assigning a test instance to a predefined category based on its features is asking you to perform classification. This is a supervised learning task where the model is trained on labeled examples so it learns how the input features map to discrete classes. When a new sample is tested, the model predicts which class it belongs to, producing a categorical outcome.

This differs from regression, which aims to predict a continuous value rather than a category. Unsupervised learning doesn’t use labeled classes to map inputs; it discovers structure or groups in the data, such as clusters, rather than assigning a specific class to each new instance. Anomaly detection focuses on identifying unusual or outlying instances, not routinely labeling every sample with a standard class, though some approaches frame it as a special one-class classification task.