Linear Regression's Dirty Six: Terms That Expose ML Hype
Think machine learning starts with neural nets? Wrong. It kicks off with linear regression—and these six terms reveal why so many 'genius' models flop.
⚡ Key Takeaways
- Linear regression boils down to six unglamorous terms that expose ML weaknesses.
- Assumptions like homoscedasticity trip up 90% of rookie models.
- History warns: Regression fueled past bubbles—watch for AI repeats.
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Originally reported by Towards AI
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