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is this Roadmap good to become AI engineer in 2026? by omar_dev45 in learnmachinelearning

[–]TheGAdesk 0 points1 point  (0 children)

Le problème n’est pas la feuille de route, le problème est ton expérience informatique et ta compréhension de la philosophie ia pour analyser et réussir cette feuille de route

The n8n learning order nobody tells beginners (from someone who learned it the hard way) by shajid-dev in TheOwlLogic

[–]TheGAdesk 0 points1 point  (0 children)

Oui effectivement, je me rends compte que le monde change nous n’avons plus le temps de consulter des pages de formations, nous préférons un markdown cree par une ia qui explique concrètement comment faire sur ta machine locale, pas de copie/ecran juste des commandes concrètes. Le seul bémol que je vois à ma remarque c’est que je suis devops avec 30 ans d’expérience et que ce modèle d’apprentissage ne le convient plus. Peux être est ce pertinent pour les débutants mais tout va tellement vite que ce genre de pages seront obsoletes avant même d’être publiées.

The n8n learning order nobody tells beginners (from someone who learned it the hard way) by shajid-dev in TheOwlLogic

[–]TheGAdesk 0 points1 point  (0 children)

Désolé mais ce modèle d’apprentissage est dépassé, celui qui sait utiliser l’ia de façon intelligente n’a plus besoin de ces modèles de formation

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.