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Visualizing thyroid disease

In previous chapters, we considered data with two or fewer features. As the number of features increases, it becomes harder to visually assess whether individual points are anomalous.

In cases where anomaly labels are available, it is important to use scatterplots to visualize how the distribution of anomalies varies with different combinations of features.

This exercise is part of the course

Introduction to Anomaly Detection in R

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Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Scatterplot showing TSH and T3
plot(___, ___, ___)
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