Why is high accuracy not always good?

Class imbalance. If 99% of cases are negative, always predicting negative gives 99% accuracy. But you've learned nothing useful. Accuracy ignores what type of errors you're making.

When should I use accuracy?

When classes are balanced and all errors are equally important. For quick model comparison in controlled settings. Always pair with other metrics for real-world evaluation.

What metrics are better than accuracy?

F1 for imbalanced classes, precision when false positives are costly, recall when false negatives are costly, AUC-ROC for ranking, or business-specific metrics tied to actual outcomes.

What is baseline accuracy?

The accuracy you'd get with a naive approach - usually predicting the majority class. Your model should beat baseline significantly. 98% accuracy isn't impressive if baseline is 97%.

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Accuracy

The proportion of correct predictions among total predictions. A basic classification metric that can be misleading for imbalanced datasets.

In-Depth Explanation

Accuracy measures the percentage of correct predictions. While intuitive, it's often inadequate alone, especially for imbalanced classes.

Accuracy formula: Accuracy = (True Positives + True Negatives) / Total Predictions

When accuracy works well:

Balanced classes (similar counts)
All errors equally important
Simple performance summary needed

When accuracy misleads:

Imbalanced classes (99% accuracy on 1% fraud detection?)
Different error costs (false negatives vs false positives)
Multi-class with varying importance

Better alternatives:

Precision and Recall
F1 Score
ROC-AUC
Confusion matrix analysis
Domain-specific metrics

Business Context

Don't be fooled by high accuracy. A fraud detector saying "not fraud" always achieves 99% accuracy but catches nothing. Choose metrics that reflect business impact.

How Clever Ops Uses This

We help Australian businesses select appropriate metrics for their AI projects, ensuring models optimise for actual business outcomes.

Example Use Case

"A model with 95% accuracy on customer churn sounds good, but if only 5% of customers churn, predicting "won't churn" for everyone achieves 95% accuracy too."

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