Evaluating an intent classifier: what I check beyond accuracy

1. 1Build a test dataset with diverse user inputs to evaluate the intent classifier
2. 2Run experiments to measure precision, recall, and F1 score beyond accuracy
3. 3Configure the classifier to handle out-of-vocabulary words and typos
4. 4Test the classifier's robustness to different user personas and languages
5. 5Apply techniques like cross-validation to ensure reliable evaluation results

NLP engineers and developers building support agents can benefit from this knowledge to improve their intent classifiers

💡 Accuracy is not enough to evaluate an intent classifier; consider precision, recall, and F1 score for better performance

🤖 Go beyond accuracy when evaluating intent classifiers! 📊