A company is using Azure Cognitive Service for Language to analyze customer support transcripts. They want to identify custom categories (e.g., 'billing', 'technical support') using a custom text classification model. After training and deploying the model, they receive many false positives for the 'billing' category. What is the best first step to improve model accuracy?
False positives for 'billing' typically stem from mislabelled training examples teaching the model incorrect boundaries. Correcting those labels addresses the root cause before tuning thresholds or adding data, making it the most effective first step for improving classification accuracy.
Why this answer
False positives for a specific category like 'billing' most often stem from mislabeled or ambiguous training examples in that category. By reviewing and correcting the training data for 'billing', you directly address the root cause of the model's confusion, which is the most effective first step in custom text classification model improvement.
Exam trap
The trap here is that candidates often jump to a threshold adjustment (Option D) as a quick fix, but Azure's custom text classification models require data quality improvements first, as confidence thresholds only affect prediction output, not model accuracy.
How to eliminate wrong answers
Option A is wrong because adding more training data to all categories indiscriminately does not target the specific false-positive issue with 'billing' and could even introduce more noise or imbalance. Option B is wrong because key phrase extraction is an unrelated Azure AI service that extracts terms, not a classification model; it cannot replace or fix a custom text classification model's accuracy. Option D is wrong because increasing the confidence threshold only filters out low-confidence predictions but does not correct the underlying misclassification pattern; it may reduce false positives at the cost of increasing false negatives, without improving model understanding.