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ML Pythonprogramming~5 mins

Anomaly detection basics in ML Python - Cheat Sheet & Quick Revision

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Recall & Review
beginner
What is anomaly detection in simple terms?
Anomaly detection is finding things that are very different from normal or usual patterns in data, like spotting a strange sound in a quiet room.
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beginner
Name two common types of anomalies.
Point anomalies (single unusual data points) and contextual anomalies (unusual data points depending on context, like a cold day in summer).
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beginner
Why is anomaly detection important in real life?
It helps find problems early, like fraud in banking, faults in machines, or unusual health signs, so we can fix them quickly.
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beginner
What is a simple method to detect anomalies?
Using a threshold on data values, for example, if temperature is usually between 20-25°C, anything above 30°C might be an anomaly.
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beginner
What role does machine learning play in anomaly detection?
Machine learning helps by learning normal patterns from data automatically and then spotting data points that don’t fit those patterns.
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What is an anomaly in data?
AA data point with zero value
BA common data point
CA data point very different from normal
DA missing value
Which of these is an example of a contextual anomaly?
AA broken sensor
BA cold day in summer
CA missing data point
DA repeated value
Why do we use anomaly detection in fraud detection?
ATo store data
BTo increase sales
CTo speed up payments
DTo find unusual transactions
Which method is simplest for detecting anomalies?
ASetting a threshold
BDeep learning
CClustering
DData cleaning
Machine learning helps anomaly detection by:
ALearning normal patterns automatically
BDeleting anomalies
CAdding noise to data
DIgnoring data
Explain what anomaly detection is and why it is useful in everyday life.
Describe two types of anomalies and give a simple example for each.