Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add topprismdata/cultivating-ml-agent --skill multi-level-aggregation-overfittinggit clone --depth 1 https://github.com/topprismdata/cultivating-ml-agentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/topprismdata/cultivating-ml-agent/multi-level-aggregation-overfitting)<a href="https://agentmods.dev/skills/topprismdata/cultivating-ml-agent/multi-level-aggregation-overfitting"><img src="https://agentmods.dev/badge/skills/topprismdata/cultivating-ml-agent/multi-level-aggregation-overfitting.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00082 | $0.00628 |
| Opus 5 | $0.00041 | $0.00314 |
| Sonnet 5 | $0.00016 | $0.00126 |
| Haiku 4.5 | $0.00008 | $0.00063 |
Grade A, and why
multi-level-aggregation-overfitting scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Level Aggregation Overfitting in Day-Specific Models
Problem
1st place solutions often use family-level and store-level aggregation features (family lag, store rolling mean, store-family ratio features). However, when applied to day-specific models (separate model per horizon day), these features can cause overfitting: CV improves but LB degrades.
Why It Overfits
In day-specific models, each model trains on ~2.87M samples covering all (store, family) pairs. The model already sees sufficient examples per pair. Family/store level features add redundancy:
-
Leakage via aggregation: Family-level lags are highly correlated with individual store-family lags (especially for families with few stores). The ratio features (
sf_to_fam_ratio) may capture noise rather than signal. -
Cross-pair interference: Day-specific models learn patterns across all pairs simultaneously. Adding aggregated features increases the feature space without adding truly independent information.
-
CV overfitting: The expanding-window CV may not penalize these features enough because the family/store patterns are stable across time, but they don't generalize to the test period's specific dynamics.
Evidence
| Version | Features | CV RMSLE | LB |
|---|---|---|---|
| R11b (base) | 82 features | 0.42041 | 0.40073 |
| R12 multilevel | 82 + 18 agg | 0.41668 (better) | 0.39874 (worse vs R11c=0.39824) |
CV improved by 0.00373 but LB got worse by 0.00050 relative to R11c.
In contrast, YoY features (only 4 features) improved LB by 0.00045 with less CV improvement.
Rule of Thumb
- Unified model: Multi-level aggregation likely helps (model needs hints about hierarchy)
- Day-specific model: Multi-level aggregation may overfit (model already sees all pairs)
- Safer alternative: Use target encoding at family/store level instead of raw aggregation
- Feature budget: Prefer fewer high-signal features (like YoY) over many correlated ones
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 61 lines · 82 tokens per session scan A c004b1d9e8ac
multi-level-aggregation-overfitting is a skill published in the GitHub repository topprismdata/cultivating-ml-agent (5 stars, last pushed 9d ago), licensed MIT. It adds 82 tokens to every session and 628 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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