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  3. Introduction to Anomaly Detection in R
  • 1

    Statistical outlier detection

    Free

    In this chapter, you'll learn how numerical and graphical summaries can be used to informally assess whether data contain unusual points. You'll use a statistical procedure called Grubbs' test to check whether a point is an outlier, and learn about the Seasonal-Hybrid ESD algorithm, which can help identify outliers when the data are a time series.

  • 2

    Distance and density based anomaly detection

    In this chapter, you'll learn how to calculate the k-nearest neighbors distance and the local outlier factor, which are used to construct continuous anomaly scores for each data point when the data have multiple features. You'll learn the difference between local and global anomalies and how the two algorithms can help in each case.

  • 3

    Isolation forest

    k-nearest neighbors distance and local outlier factor use the distance or relative density of the nearest neighbors to score each point. In this chapter, you'll explore an alternative tree-based approach called an isolation forest, which is a fast and robust method of detecting anomalies that measures how easily points can be separated by randomly splitting the data into smaller and smaller regions.

  • 4

    Comparing performance

    You've now been introduced to a few different algorithms for anomaly scoring. In this final chapter, you'll learn to compare the detection performance of the algorithms in instances where labeled anomalies are available. You'll learn to calculate and interpret the precision and recall statistics for an anomaly score, and how to adapt the algorithms so they can accommodate data with categorical features.

Initializing

Exercise

Anomaly contours

You now have the key ingredients to produce a contour plot, which is a powerful tool for viewing how the anomaly score varies across the region spanned by the data points.

The wine_grid data from the previous exercise has been preloaded for you to use.

Instructions

100 XP
  • Use the contourplot() function to generate a contour plot of the score.
  • Ensure that the pH variable is shown on the y-axis and alcohol on the x-axis.