Say you have 5,000 examples of normal airplane engines, and 15 examples of anomalous engines. How would you use the 15 examples of anomalous engines to evaluate your anomaly detection algorithm?
Select one:
- You cannot evaluate an anomaly detection algorithm because it is an unsupervised learning algorithm.
- Because you have data of both normal and anomalous engines, don’t use anomaly detection. Use supervised learning instead.
- Use it during training by fitting one Gaussian model to the normal engines, and a different Gaussian model to the anomalous engines.
- Put the data of anomalous engines (together with some normal engines) in the cross-validation and/or test sets to measure if the learned model can correctly detect anomalous engines.
- Correct:
Anomalous examples are used to evaluate rather than train the model.
