The Challenge: Interpreting Accuracy with Imbalanced Data
You might be here because an evaluation in Plexus highlighted a class imbalance in your dataset. This is a common situation where some categories (or classes) of data are far more frequent than others. For example, in a dataset of emails, "normal" emails might vastly outnumber "spam" emails. Or, in manufacturing, non-defective items might be much more common than defective ones.
While having imbalanced data isn't an error in itself, it can make traditional accuracy scores highly misleading. Let's explore why class imbalance is a critical factor in understanding your classifier's true performance and how to interpret evaluation metrics correctly in these scenarios.
When Accuracy Deceives: The Majority Class Trap
The primary issue with class imbalance is that a classifier can achieve a high accuracy score by simply always predicting the majority class, even if it has learned nothing about distinguishing between classes, especially the rare ones. This creates a false sense of good performance.
The 'Always Safe' Email Filter (97% Safe, 3% Prohibited)
Strategy: Label ALL emails as 'Safe'. Actual Data: 970 Safe, 30 Prohibited.
You achieved 97% accuracy:
Raw Accuracy: 97% !
Seems Great, But It's Critically Flawed!
This filter detects ZERO prohibited emails. It appears accurate only because it correctly labels the 97% majority class.
In the example above, a filter that marks every email as "safe" achieves 97% accuracy. This sounds impressive! However, it completely fails at its primary task: identifying prohibited content. The high accuracy comes purely from the data imbalance.
Stacked Deck (75% Red Cards) - Always Guess Red
Strategy: Always predict 'Red'. Actual Deck: 75% Red, 25% Black.
You achieved 75% accuracy:
Raw Accuracy: 75% !
Also Deceptive!
This 75% accuracy is achieved by simply guessing the majority class. It reflects the data distribution, not predictive skill on black cards.
Key Insight: Imbalance Inflates Naive Accuracy
Raw accuracy scores are profoundly misleading with imbalanced data. A high accuracy might simply reflect the proportion of the majority class, not genuine predictive capability across all classes. What seems like excellent performance could indicate a model that has learned very little, or worse, is completely ineffective for minority classes.
Solution: Clarity Through Contextual Gauges
To cut through the confusion caused by class imbalance, it's essential to use evaluation tools that provide proper context. Plexus employs a two-pronged approach:
- Contextualized Accuracy Gauges: The colored segments of the Accuracy gauge dynamically adjust based on the class distribution (including imbalance and number of classes). This means the definition of "good," "viable," etc., shifts to reflect the true baseline for that specific imbalanced dataset. An accuracy of 97% might be chance-level for a 97/3 split, and the gauge will show this.
- Inherently Context-Aware Agreement Gauges: Metrics like Gwet's AC1 (used in the Agreement gauge) are designed to correct for chance agreement. They inherently account for class imbalance. An AC1 score of 0.0 indicates performance no better than chance (e.g., always guessing the majority class), regardless of how high the raw accuracy appears.
Let's revisit our examples, this time with both gauges active, showing how they reveal the truth:
Visualizing How Contextual Gauges Adapt to Imbalance (65% Accuracy Example)
The examples below all show an accuracy of 65%. The top gauge in each column uses a fixed, uncontextualized scale. The bottom gauge dynamically adjusts its segments based on the specific class imbalance described. This illustrates how the interpretation of the same 65% accuracy score changes dramatically when the gauge reflects the underlying data distribution.
Balanced (50/50)
65% is somewhat above the 50% chance baseline.
Imbalanced (75/25)
65% is below the 75% majority baseline; poor performance.
3-Class Imbal. (80/10/10)
65% is below the 80% majority baseline; poor for this setup.
Highly Imbal. (95/5)
65% is far below the 95% majority baseline; very poor.
This visualization demonstrates that the Plexus Accuracy Gauge helps you avoid being misled by a raw accuracy percentage. By adapting its scale, it correctly shows that a 65% accuracy can range from mediocre (in a balanced scenario) to very poor (in highly imbalanced scenarios where simply guessing the majority class would yield a higher score).
Now, let's look at the full `EvaluationCard` examples again, which combine the contextualized Accuracy Gauge with the Agreement Gauge for a complete picture:
The 'Always Safe' Email Filter - Plexus View
97/3 Imbalance. Strategy: Always predict 'Safe'. Raw Accuracy: 97%.
Key Insight:
The Agreement gauge (AC1=0.0) immediately shows ZERO predictive skill beyond chance. The contextualized Accuracy gauge confirms this: 97% is the baseline for this dataset; true skill would require higher. Both expose the filter as useless for finding prohibited content.
Stacked Deck (75% Red) - Plexus View
75/25 Imbalance. Strategy: Always predict 'Red'. Raw Accuracy: 75%.
Key Insight:
Despite 75% accuracy, the Agreement gauge (AC1=0.0) again correctly shows no predictive skill. The contextualized Accuracy gauge also marks 75% as the baseline chance level for this specific imbalance. No real learning has occurred.
The Power of Two Gauges with Imbalanced Data
- The Contextualized Accuracy Gauge adjusts its scale to show what raw accuracy truly means given the imbalance.
- The Agreement Gauge (Gwet's AC1) provides a single, chance-corrected score. An AC1 of 0.0, as seen in these "always guess majority" scenarios, definitively indicates no skill beyond chance, regardless of a high raw accuracy.
If you see a high accuracy but an Agreement score near zero, it's a strong indicator that class imbalance is distorting the accuracy metric, and the model may not be performing well on minority classes.
What About Moderately Imbalanced Data?
The principles are the same for less extreme imbalances. Consider our Article Topic Labeler, which has a 40% majority class (News) among 5 classes. This is imbalanced, though not as drastically as the 97/3 scenario.
Article Topic Labeler - Moderate Imbalance (40% News)
5-class, 40% News. Accuracy: 62%, Gwet's AC1: 0.512
Key Insight:
Here, the AC1 of 0.512 indicates moderate skill beyond chance. The contextualized Accuracy gauge shows 62% as 'good' for this specific 5-class imbalanced problem—better than just guessing 'News' (40% accuracy) or random 5-class (20% accuracy). Both gauges provide a consistent, nuanced view that accounts for the imbalance.
Even with moderate imbalance, relying solely on accuracy could be misleading. The combination of contextualized accuracy and a chance-corrected agreement score provides a more trustworthy assessment.
For a Comprehensive Overview
This guide focuses on the "class imbalance" problem. For a broader understanding of how Plexus addresses various contextual factors in evaluation (including the number of classes and the full two-pronged solution strategy), please see our main guide:
Next Steps
Continue exploring our documentation for a deeper understanding of evaluation: