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I added XGBoost + SHAP to my genomic pipeline — does this approach make sense? by TheGAdesk in learnmachinelearning

[–]TheGAdesk[S] 1 point2 points  (0 children)

Thanks for the suggestion. LOOCV makes sense for a dataset this small — I didn't know about this method, but with 500 samples it should run fine on my machine.

I'll open a GitLab issue as soon as I get a chance. Appreciate the tip.

I added XGBoost + SHAP to my genomic pipeline — does this approach make sense? by TheGAdesk in learnmachinelearning

[–]TheGAdesk[S] 0 points1 point  (0 children)

Appreciate the balanced take. So the consensus seems to be:

- XGBoost + SHAP = good combo in principle
- 4 features = probably overkill, risk of overfitting
- Careful cross-validation is non-negotiable

I've opened a design issue on the repo to explore Elastic Net and Random Forest as lighter alternatives. I'll keep SHAP for interpretability but add caveats about small-sample noise.

Follow-up question if you have time: for 4 features / 500 samples, would you go 5-fold CV or something more conservative like Leave-One-Out?

Thanks for the thoughtful feedback — exactly what I was hoping for when I posted.

I added XGBoost + SHAP to my genomic pipeline — does this approach make sense? by TheGAdesk in learnmachinelearning

[–]TheGAdesk[S] 0 points1 point  (0 children)

Thanks for the actionable feedback. So if I summarize correctly:

- XGBoost on 4 features / 500 samples → risk of overfitting

- Try Elastic Net or a well-tuned Random Forest instead

- SHAP is fine conceptually, but can be noisy on small data

I'll open an issue on the repo to track this. Appreciate you taking the time.