Why is accuracy sometimes misleading?
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✅ What is Accuracy in Classification?
Definition:
In classification, accuracy measures how often the model’s predictions are correct compared to the total number of predictions.
It is calculated as:
Or in terms of confusion matrix (for binary/multiclass classification):
Where:
TP = True Positives
TN = True Negatives
FP = False Positives
FN = False Negatives
🔹 Example
Suppose you have 100 test samples for spam detection:
Correctly classified as spam (TP) = 40
Correctly classified as not spam (TN) = 50
Wrongly classified as spam (FP) = 5
Wrongly classified as not spam (FN) = 5
✅ Limitations of Accuracy
Works well if classes are balanced.
Can be misleading in imbalanced datasets.
Example: In a dataset with 95% "not spam" and 5% "spam", a model that predicts "not spam" for everything gets 95% accuracy — but it completely fails to detect spam.
📌 Short Interview Answer
“Accuracy in classification is the ratio of correct predictions (true positives + true negatives) to the total predictions. It tells how often the model is right, but it can be misleading for imbalanced datasets, where metrics like precision, recall, or F1-score give a better picture.”
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