What is outlier detection in machine learning?
Outlier detection is the process of identifying data points that differ significantly from the majority of the data.
Why is outlier detection important?
It's crucial for improving model accuracy, detecting fraud, and ensuring data quality.
What techniques are commonly used for outlier detection?
Common techniques include statistical tests, clustering methods, and machine learning algorithms like Isolation Forest.
Can you give an example of outlier detection in a real-world scenario?
Sure! In finance, outlier detection can identify fraudulent transactions that deviate from normal spending patterns.
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