With k-means clustering models behind us, it is now time to dive into anomaly detection models. Anomaly detection is one of the newer additions to ML.NET, and specifically, time-series transforms. In this chapter, we will dive into anomaly detection and the various applications best suited to utilizing anomaly detection. In addition, we will build two new example applications: one anomaly detection application that determines whether the login attempt is abnormally demonstrating the randomized PCA trainer, and one that demonstrates time series in a network traffic anomaly detection application. Finally, we will explore how to evaluate an anomaly detection model with the properties that ML.NET exposes.
In this chapter, we will cover the following topics:
- Breaking down anomaly detection
- Creating a time series application
- Creating an anomaly detection application...
