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Anomaly score

Your visualization suggested that thyroid disease could be detected from anomalous hormone measurements.

In this exercise you'll use an isolation forest to generate an anomaly score for thyroid levels, and compare the resulting score against the true disease status.

This exercise is part of the course

Introduction to Anomaly Detection in R

View Course

Exercise instructions

  • Fit an isolation forest to the thyroid hormone measurements.
  • Generate anomaly scores for the thyroid data and append the result to thyroid as the new column iso_score.
  • Use the boxplot() function to compare the score distribution for patients with and without thyroid disease, using the label column.

Hands-on interactive exercise

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

# Fit isolation forest
thyroid_forest <- iForest(___, ___ = 200, phi = 100)

# Anomaly score 
thyroid$iso_score <- predict(thyroid_forest, ___)

# Boxplot of the anomaly score against labels
boxplot(___ ~ ___, ___, col = "olivedrab4")
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