Over the course of this chapter, we discussed ML.NET's anomaly detection support via the randomized PCA algorithm. We also created and trained our first anomaly detection application using the randomized PCA algorithm to predict abnormal logins. In addition to this, we created a time series application, looking at network traffic and finding spikes in the amount of transferred data. Finally, we also looked at how to evaluate an anomaly detection model and the various properties that ML.NET exposes to achieve a proper evaluation of an anomaly detection model.
In the next chapter, we will deep dive into matrix factorization with ML.NET to create a music preference predictor.
