# Spam Classifier: Questions

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## 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:*

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## 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:*

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## Checkpoint 3: Designing Features by Hand

**8. List all the features you implemented and the reasoning behind each:**

| Feature name | What it measures | Reasoning |
|-------------|-----------------|-----------|
| | | |
| | | |
| | | |

**9. Record your best results:**

| Metric | Value |
|--------|-------|
| Spam precision | |
| Spam recall | |
| Spam F1 | |

**10. Which features received the largest positive weights (most predictive of spam)? The largest negative weights (predictive of ham)? Does this match your expectations?**

*Your answer:*

**11. Did any feature you thought would help receive a near-zero weight? Why might the model have decided it was unimportant?**

*Your answer:*

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## Checkpoint 4: Bag of Words

**12. Which transformers did you include in your cleaning pipeline, and in what order? Explain how each one changes the vocabulary.**

*Your answer:*

**13. Record your best results:**

| Metric | Value |
|--------|-------|
| Spam precision | |
| Spam recall | |
| Spam F1 | |

**14. How did the bag-of-words classifier's performance compare to your best hand-designed-features classifier? What do you think accounts for the difference?**

*Your answer:*

**15. Look at the words with the strongest weights (in either direction). Do any surprise you? What do they suggest about how the model is making its decisions?**

*Your answer:*

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## Final Questions

**16. 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 |
|-------------|-----------------|------------------|
| | | |
| | | |
| | | |
| | | |
| | | |

**17. Could adding more features ever *hurt* the performance of a classifier? Explain
when and why this might happen.**

*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:*