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Thyroid precision and recall

Cross-tabulating the agreement between a binary score and a known label is a great way to understand how well the algorithm performs. Precision and recall are two further measures based on the table that give more insight into how well the score performs.

In this exercise, you'll explore precision and recall using the thyroid data. The binary_lof and binary_iso scores created in the previous exercises are available to use as columns in the thyroid data. The code used to tabulate agreements in the previous exercise is also included.

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.

# Tabulation for the binary isolation score
table(thyroid$label, thyroid$binary_iso)
# Precision for the isolation scores
precision_iso <- ___ / ___

# Tabulation for the binary lof score
table(thyroid$label, thyroid$binary_lof)
# Precision for the binary lof score
precision_lof <- ___ / ___
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