Classifier Metrics | Stanford CS224U Natural Language Understanding | Spring 2021 | Summary and Q&A

TL;DR

Choosing the right classifier metric is crucial and depends on the goals of your experiment, with accuracy being the most famous but limited metric.

Key Insights

• 📈 Choosing the right classifier metric is crucial as different metrics encode different values and goals for your system.
• 🏛️ Accuracy is the most famous metric, but it fails to control for class size imbalance.
• 🏛️ Precision focuses on correct predictions for a class, while recall emphasizes capturing all true instances of a class.
• 💯 F scores combine precision and recall, providing a balance based on the weighting value (beta).
• 💯 Macro averaging, weighted averaging, and micro averaging are different ways to summarize multiple F scores into a single number, each with its own assumptions and weaknesses.
• ™️ Precision-recall curves offer a different perspective by plotting the trade-off between precision and recall for different thresholds.

Transcript

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Questions & Answers

Q: Why is choosing the right metric important for experimental work?

Choosing the right metric is important because different metrics encode different values and goals, allowing you to evaluate your system's performance based on specific objectives.

Q: How does accuracy measure classifier performance?

Accuracy measures the number of correct predictions divided by the total number of examples, but it fails to account for class size imbalance and does not provide a per-class notion of accuracy.

Q: What are precision and recall metrics?

Precision measures the correct predictions for a class divided by the sum of all guesses for that class. Recall measures the correct predictions for a class divided by the sum of all true instances of that class.

Q: How are F scores calculated and what do they represent?

F scores are harmonic means of precision and recall, with a weighting value (beta) to control the balance. They represent the alignment of predictions with true instances for a given class.

Summary & Key Takeaways

• Metrics encode different values and goals for your system and hypothesis.

• Accuracy, precision, recall, and F scores are commonly used classifier metrics.

• Accuracy measures how often the system is correct, but it fails to control for class size imbalance.