How do different outlier detection algorithms compare?

How do different outlier detection algorithms compare?

There are several outlier detection algorithms, each with its strengths and weaknesses. Common ones include Z-Score, IQR, and DBSCAN. Would you like a detailed comparison?

Yes, please explain the Z-Score and IQR methods.

The Z-Score method identifies outliers by measuring how many standard deviations a data point is from the mean. In contrast, the IQR method uses the interquartile range to determine outliers, focusing on the middle 50% of the data.

What about DBSCAN? How does it differ from those?

DBSCAN is a density-based algorithm that groups points closely packed together and marks points in low-density regions as outliers. Unlike Z-Score and IQR, it doesn't assume a specific distribution.

Which algorithm would you recommend for large datasets?

For large datasets, DBSCAN is often recommended due to its efficiency in handling large volumes of data and its ability to find clusters of varying shapes.