Question

A Machine Learning Technique That Helps In Detecting The Outliers In Data.

Answer 1

Explanation:

Dbscan (Density Based Spatial Clustering of Applications with Noise) In machine learning and data analytics clustering methods are useful tools that help us visualize and understand data better. ... A cluster can be defined as the maximal set of 'density connected points' in the feature space.

Answer 2

The machine learning algorithm which helps in detecting the outliers is known as anomaly detection.

• Local outlier factor is probably the most common technique for anomaly detection.
• This algorithm is based on the concept of local density. It compares the local density of an object with that of its neighboring data points.
• If a data point has a lower density than its neighbors, then it is considered an outlier
• Anomaly detection systems are either manually built by experts setting thresholds on data or constructed automatically by learning from the available data through machine learning which finds the outliers of a dataset that doesn't belong
• It helps in monitoring the cause of chaos engineering by detecting outliers and informing the responsible parties to act
• Therefore, the right option is anomaly detection.

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