This chapter covers techniques to detect outliers in 1-dimensional data using histograms, scatterplots, box plots, z-scores, and modified z-scores.
In this chapter, you’ll learn the ins and outs of how the Isolation Forest algorithm works. Explore how Isolation Trees are built, the essential parameters of PyOD's IForest and how to tune them, and how to interpret the output of IForest using outlier probability scores.
After a tree-based outlier classifier, you will explore a class of distance and density-based detectors. KNN and Local Outlier Factor classifiers have been proven highly effective in this area, and you will learn how to use them.
In this chapter, you’ll learn how to perform anomaly detection on time series datasets and make your predictions more stable and trustworthy using outlier ensembles.