8 Evaluating detectors and parameters
This chapter covers
- The effect of the k parameter used with k nearest neighbors and local outlier factor
- Techniques to evaluate detectors
- Evaluating the similarity in scores between detectors
- Using synthetic data to test outlier detectors
- Comparing the train and predict times for detectors under different workloads
Now that we’ve described a number of outlier detection algorithms, the question you’ll face is: Which is the best detector, or the best set of detectors, to use for your projects? We can’t answer this completely, as each detector will be more appropriate in some circumstances than others, but we will go through some methods to help compare outlier detectors.