Feature Engineering Trade-offs & Multi-Metric
Article summary
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Adding manual signal features (Impact Severity, Energy Ratios) boosted Class 2 F1 score but caused metric drop in Class 0 & 1, decreasing total leaderboard performance. Root Causes Multicollinearity : Re-creating Peak-to-RMS ratio when** Crest Factor** was already present. - Dimensional Mismatch : Direct summation of unscaled velocity (mm/s) and acceleration (g). -** Outlier Explosion **: Division by small RMS values creating extreme data spikes. Metric Trade-off : Optimizing features for a…
1Key Takeaways
- Adding manual signal features (Impact Severity, Energy Ratios) boosted Class 2 F1 score but caused metric drop in Class 0 & 1, decreasing total leaderboard performance.
- Root Causes Multicollinearity : Re-creating Peak-to-RMS ratio when** Crest Factor** was already present.
- - Dimensional Mismatch : Direct summation of unscaled velocity (mm/s) and acceleration (g).
- -** Outlier Explosion **: Division by small RMS values creating extreme data spikes.
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that adding manual signal features (Impact Severity, Energy Ratios) boosted Class 2 F1 score but caused metric drop in Class 0 & 1, decreasing total leaderboard performance.
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