What are the different techniques for outlier detection?

What are the different techniques for outlier detection?

There are several techniques for outlier detection, including statistical methods, clustering-based methods, and machine learning approaches. Would you like a detailed comparison?

Yes, please compare statistical methods and machine learning approaches.

Statistical methods, like Z-score and IQR, are simple and effective for normally distributed data. Machine learning methods, such as Isolation Forest and One-Class SVM, can handle complex patterns and large datasets better. However, they require more computational resources.

Which method is more effective in practice?

It depends on the dataset and the specific use case. For smaller, well-defined datasets, statistical methods may suffice. For larger, more complex datasets, machine learning methods often yield better results.

Can you give examples of when to use each method?

Sure! Use statistical methods for small datasets with a known distribution, like quality control. Use machine learning methods for large datasets with unknown distributions, like fraud detection in financial transactions.