How do I choose the right outlier detection technique?

What factors should I consider when choosing an outlier detection technique?

You should consider the nature of your data, the type of outliers you're looking for, and the algorithm's complexity.

Can you explain the difference between supervised and unsupervised outlier detection?

Sure! Supervised methods use labeled data to identify outliers, while unsupervised methods do not require labels and find anomalies based on data distribution.

What are some common techniques for outlier detection?

Common techniques include Z-score, Isolation Forest, DBSCAN, and Local Outlier Factor. Each has its strengths depending on the data.

How do I evaluate the effectiveness of an outlier detection technique?

You can evaluate effectiveness using metrics like precision, recall, and F1-score, or by visualizing the results to see if they make sense.