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questions.md

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+# Spam Classifier: Questions
+
+---
+
+## Checkpoint 1: Exploring the Dataset
+
+**1. How many messages are in the dataset? How many are ham, and how many are spam?**
+
+*Your answer:*
+
+**2. Look at at least ten spam messages (`df[df.label == "spam"]`). List three patterns you notice.**
+
+1.
+2.
+3.
+
+**3. Look at at least ten ham messages (`df[df.label == "ham"]`). How do they differ from spam?**
+
+*Your answer:*
+
+---
+
+## Checkpoint 2: Manual Classifier
+
+**4. What rules did you write? List each rule and the pattern it targets.**
+
+*Your answer:*
+
+**5. Record your best results:**
+
+| Metric | Value |
+|--------|-------|
+| Spam precision | |
+| Spam recall | |
+| Spam F1 | |
+
+**6. Does your classifier make more false positives (ham flagged as spam) or false negatives (spam missed)?**
+
+*Your answer:*
+
+**7. Describe one rule you tried that did not help and explain why.**
+
+*Your answer:*
+
+---
+
+## Checkpoint 3: Data Cleaning
+
+**8. Which transformers did you add to the pipeline, and in what order?**
+
+*Your answer:*
+
+**9. Did any transformer hurt performance? Why might cleaning sometimes make things worse?**
+
+*Your answer:*
+
+**10. If you removed the lowercasing from your `predict_one` method now that the pipeline handles it, would your results change?**
+
+*Your answer:*
+
+---
+
+## Checkpoint 4: Feature Engineering
+
+**11. List all the features you implemented and the reasoning behind each:**
+
+| Feature name | What it measures | Reasoning |
+|-------------|-----------------|-----------|
+| | | |
+| | | |
+| | | |
+
+**12. Record your best results:**
+
+| Metric | Value |
+|--------|-------|
+| Spam precision | |
+| Spam recall | |
+| Spam F1 | |
+
+**13. Which features received the largest positive weights (most predictive of spam)?**
+
+*Your answer:*
+
+**14. Which features received near-zero weights? Why might the model have ignored them?**
+
+*Your answer:*
+
+---
+
+## Final Questions
+
+**15. Pick a different classification problem (for example: positive vs. negative movie reviews,
+news articles vs. opinion pieces, or medical vs. general-audience text).
+Propose five features you would extract to classify it, and explain your reasoning.**
+
+Problem I chose:
+
+| Feature name | What it measures | Why it might help |
+|-------------|-----------------|------------------|
+| | | |
+| | | |
+| | | |
+| | | |
+| | | |
+
+**16. Could adding more features ever *hurt* the performance of a classifier? Explain
+when and why this might happen.**
+
+*Your answer:*
+
+**17. It would be possible to make every word in the vocabulary a feature, where each
+feature indicates whether that word appears in the message. Explain how this could
+be implemented. Do you think it would work well? What are the trade-offs?**
+
+*Your answer:*
+
+**18. In this lab you split the data into 70% training and 30% testing. What would happen
+if you used 99% for training and 1% for testing? What about 1% for training and 99%
+for testing?**
+
+*Your answer:*