Evaluating AI Classifiers on Real Ticket Data: Metrics That Matter

Introduction

Support ticket data is messy and often heavily skewed toward a few common categories. For example, 80% of tickets might be labeled “general inquiry”, making classifiers biased toward the majority class. In practice, ML on ticket data may be used for:

• Priority prediction (e.g. flagging urgent issues)
• Queue or team assignment (e.g. sending billing questions to finance)
• Intent or topic classification (e.g. “feature request” vs “bug report”)

These use cases show why evaluation is challenging: real-world ticket datasets are multi-class and multi-label, with noisy text and imbalanced classes:contentReference[oaicite:0]{index=0}. A naïve model that always predicts the majority class can still score high accuracy by ignoring rare but important cases. We’ll examine why accuracy alone is misleading and discuss the metrics that truly matter.

Why Accuracy is Misleading

Accuracy is defined as the total correct predictions over all predictions: contentReference[oaicite:1]{index=1}: [ \text{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN} ] In formula terms, accuracy = (TP + TN)/(all samples):contentReference[oaicite:2]{index=2}. While simple, accuracy fails badly on imbalanced data. For example, if 80% of tickets belong to class A, a dumb classifier that always predicts A achieves 80% accuracy by default – yet it completely ignores the other 20% of tickets. In extreme cases (e.g. 99% vs 1% class split), always predicting the majority yields 99% accuracy despite no real learning:contentReference[oaicite:3]{index=3}. In short, a high accuracy can simply reflect class distribution, not genuine performance.

“... accuracy is no longer a proper measure [for imbalanced datasets], since it does not distinguish between the numbers of correctly classified examples of different classes. Hence, it may lead to erroneous conclusions ...”:contentReference[oaicite:4]{index=4}.

Core Metrics: Precision, Recall, F1

To evaluate classifiers under imbalance, we rely on precision, recall, and F1-score, which focus on errors in minority classes. These are derived from the confusion matrix, e.g. for binary classification:

	Predicted Positive	Predicted Negative
Actual Positive	True Positive (TP)	False Negative (FN)
Actual Negative	False Positive (FP)	True Negative (TN)

From these counts, we define:

• Precision = TP / (TP + FP) – proportion of predicted positives that are correct: contentReference[oaicite:5]{index=5}.
• Recall = TP / (TP + FN) – proportion of actual positives that were found: contentReference[oaicite:6]{index=6}.
• F1-Score = harmonic mean of precision and recall:contentReference[oaicite:7]{index=7}: [ \mathrm{F1} = \frac{2 \cdot \mathrm{TP}}{2 \cdot \mathrm{TP} + \mathrm{FP} + \mathrm{FN}}. ]

Each metric highlights different errors: precision penalizes false alarms (FP), while recall penalizes misses (FN). The F1-score balances both. For completeness, note that accuracy can also be written as ( (TP + TN) / (TP+TN+FP+FN) ):contentReference[oaicite:8]{index=8}, but on imbalanced data it masks model failures.

In practice, scikit-learn’s classification_report computes these per class. For example:

from sklearn.metrics import classification_report

print(classification_report(y_test, y_pred))

reports precision, recall, F1 (and support) for each ticket class.

Macro vs Micro Averaging

For multi-class problems, metrics can be averaged in different ways. Micro-averaging pools all classes together by summing global TP, FP, FN, then computing metrics – effectively weighting by each class’s support. Macro-averaging computes the metric for each class separately and then takes the unweighted mean. In other words, macro treats all classes equally (so rare classes count as much as common ones), while micro favors performance on frequent classes. Use macro-averaging when minority classes are critical (e.g. catching a rare urgent ticket), and micro-averaging when overall accuracy across all tickets is more important.

Averaging	How It’s Computed	When to Use
Micro	Global counts of TP, FP, FN across all classes	Gives overall performance (favors large classes)
Macro	Average of each class’s metric (each class weighted equally)	Ensures small/rare classes count equally

Multi-Label Challenges

Helpdesk tickets often carry multiple labels at once (e.g. a ticket might have both a queue and a priority label). In multi-label setups, additional metrics apply:

• Subset Accuracy (Exact Match) – fraction of samples where all predicted labels exactly match the true set of labels. This is very strict: one wrong label means failure.
• Hamming Loss – the fraction of individual label predictions that are incorrect. Hamming loss is more forgiving: each label is judged independently. A lower Hamming loss (near 0) is better.
• Label Ranking Loss – measures how many label pairs are incorrectly ordered by confidence. It’s relevant when the model outputs scores for each label, and we care about ranking labels for each ticket.

Scikit-learn provides functions like accuracy_score (subset accuracy in multi-label mode) and hamming_loss. In general, one chooses the metric that aligns with business needs: exact match if you need all labels correct, or Hamming/Ranking loss if partial correctness is acceptable.

Confusion Matrix in Practice

A confusion matrix is often the first look at classifier behavior. In Python you can compute and display it with scikit-learn:

from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay

cm = confusion_matrix(y_test, y_pred, labels=classes)
print("Confusion Matrix:\n", cm)

# To visualize:
ConfusionMatrixDisplay(cm, display_labels=classes).plot()

Here cm[i, j] is the number of tickets whose true class is i but were predicted as class j. When inspecting a confusion matrix (or its heatmap), look for:

• Off-diagonal cells – these indicate misclassifications (which classes are most often confused).
• False positives vs false negatives – e.g. a high row-sum off-diagonal means the model frequently missed that actual class (many FNs); a high column-sum off-diagonal means many incorrect predictions of that class (FPs).
• Underrepresented classes – classes with few examples may show up as nearly empty rows/columns, indicating the model rarely predicts them correctly.

Properly analyzing the confusion matrix helps target data cleaning or model adjustments for specific ticket types.

Evaluation Strategy for Real Ticket Systems

Building a reliable evaluation pipeline requires more than just picking metrics:

• Clean, labeled data: Ensure your test set is representative and accurately labeled. Remove duplicates or mislabeled tickets before evaluating.
• Baseline vs Fine-tuned: Always compare your AI model to simple baselines (e.g. majority-class predictor, or keyword rule systems). Measure relative improvements using the chosen metrics.
• Periodic Reevaluation: Ticket trends change over time (seasonal issues, new products). Plan to retrain and re-evaluate the model regularly or trigger on data drift.
• Stakeholder Communication: Translate metrics into actionable insights for non-technical stakeholders. For example, "Recall rose from 75% to 85% for urgent tickets, meaning we catch 10% more high-priority issues automatically." Use charts (e.g. bar plots of precision/recall per class) and emphasize business impact (faster response, reduced backlogs).

Conclusion

In summary, you can’t improve what you don’t measure. Accuracy alone isn’t enough for imbalanced, complex ticket data. Instead, track class-wise precision, recall, and F1 (using macro/micro averages as appropriate), and consider multi-label metrics if your tickets have multiple annotations. Start metric tracking early in any AI integration so that gains (or problems) are visible. By focusing on the right metrics from day one, support teams can iteratively improve their ticket classifiers and deliver more reliable automation.

Want to try these ideas on your own data? Check out Softoft’s Ticket Classification platform to experiment with real ticket datasets and built-in evaluation tools.