Consider the use of accuracy, precision, recall a funny classification problem in a medical scenario set in a forest involving forest animals.
So, the data has:
Total Forest Animals Tested: 1000
Animals with Silly Squirrel Syndrome (SSS): 20 (positive class)
Animals without Silly Squirrel Syndrome: 980 (negative class)
Model Predictions:
- True Positives (TP): 15 (animals correctly detected with SSS)
- False Positives (FP): 50 (healthy animals incorrectly identified as having SSS)
- True Negatives (TN): 930 (healthy animals correctly identified)
- False Negatives (FN): 5 (animals with SSS missed)
Calculations:
- Accuracy:
- Precision:
- Recall:
Interpretation:
- Accuracy (94.5%): The forest medical team is happy because the model correctly identifies 94.5% of the forest animals. However, this high accuracy is mainly due to the large number of healthy animals.
- Precision (23.1%): When the model predicts an animal has Silly Squirrel Syndrome, it is only correct 23.1% of the time. This means a lot of healthy animals like chipmunks and deer are incorrectly told they have SSS, leading to unnecessary acorn diets and tail-wagging therapy.
- Recall (75%): The model correctly identifies 75% of the animals with SSS, which means it is pretty good at catching those sneaky, silly squirrels, but still misses a few who continue their hilarious antics unnoticed.
Importance:
In this forest scenario, precision is crucial because a low precision means many healthy animals are wrongly diagnosed with Silly Squirrel Syndrome, leading to unnecessary and amusing treatments. Recall is also important because a low recall means many cases of the syndrome are missed, allowing some silly squirrels to go on confusing the other animals. Therefore, precision and recall provide a better evaluation of the model’s performance in this whimsical woodland setting than accuracy alone.
