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Chris Proctor
2026-06-08 14:02:37 -04:00
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**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?** **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:* *Your answer:*
---
## 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:*