What are some advanced techniques for outlier detection?

What are some advanced techniques for outlier detection?

There are several advanced techniques for outlier detection, including Isolation Forest, DBSCAN, and One-Class SVM. Would you like to know more about any specific method?

Can you explain how Isolation Forest works?

Isolation Forest works by isolating observations in the data. It creates random partitions and identifies anomalies as those that require fewer partitions to isolate. This method is efficient for high-dimensional datasets.

What are the advantages of using DBSCAN for outlier detection?

DBSCAN is great for detecting outliers because it can find clusters of varying shapes and sizes, and it doesn't require specifying the number of clusters in advance. It also effectively handles noise.

Are there any limitations to these techniques?

Yes, each technique has its limitations. For example, Isolation Forest may struggle with very high-dimensional data, while DBSCAN can be sensitive to the choice of parameters. It's important to choose the right method based on your data characteristics.