MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Anomaly detection is the process of identifying unexpected items or events in datasets, which differ from the norm. In contrast to standard classification tasks, anomaly detection is often applied on unlabeled data, taking only the internal structure of the dataset into account.

Detailed explanation-2: -Anomaly detection is the process of identifying unexpected items or events in data sets, which differ from the norm. And anomaly detection is often applied on unlabeled data which is known as unsupervised anomaly detection.

Detailed explanation-3: -Anomaly detection is the process of analyzing company data to find data points that don’t align with a company’s standard data pattern. Companies use anomalous activity detection to define system baselines, identify deviations from that baseline, and investigate inconsistent data.

Detailed explanation-4: -How to detect Anomalies? Simple statistical techniques such as mean, median, quantiles can be used to detect univariate anomalies feature values in the dataset. Various data visualization and exploratory data analysis techniques can be also be used to detect anomalies.