Kaggle Discussion Search (Community Knowledge)

Context

Kaggle discussions contain non-official, practitioner-tested wisdom that often beats academic papers in practical value. Top solution write-ups, hyperparameter choices, CV-LB gap analyses — all are public. This skill teaches the agent to mine this gold mine at decision time, especially when starting a new competition or validating an existing skill against community evidence.

The core insight: paper says "5-10% improvement", Kaggle discussion says "actually 2% on LB, watch out for X". Different signal, complementary.

Guidance

Basic Usage

from framework.src.knowledge import KaggleForum

kf = KaggleForum()
# Get top-voted discussions for a specific competition
top_discs = kf.get_top_solutions("spaceship-titanic", top_n=20)

for d in top_discs:
    print(f"[{d.votes}↑ {d.comments_count}💬] {d.title}")
    print(f"  by {d.author}: {d.url}")

Search by Keyword

# Find discussions mentioning a specific technique
discs = kf.search_competition(
    competition_slug="store-sales-time-series-forecasting",
    query="CatBoost ensemble",
    sort_by="votes"
)

Global Search (Cross-Competition)

# Search across all Kaggle discussions
discs = kf.search_global("CV-LB gap", max_results=20)

Decision Workflow

Starting new Kaggle competition?
  ↓
1. kf.get_top_solutions(competition_slug, top_n=30)
   → Read the top-voted discussions
   → Note: hyperparameter choices, CV strategies, common pitfalls
  ↓
2. kf.search_competition(slug, query="LB score CV correlation")
   → Find CV-LB gap analysis specific to this competition
  ↓
3. Cross-reference with skills:
   → cv-lb-gap-acknowledgment (general principle)
   → time-series-walk-forward-validation (if time series)
  ↓
4. After submission, search for related discussions:
   → kf.search_competition(slug, query="your technique here")
   → Validate your approach

Combine with Papers

from framework.src.knowledge import KnowledgeAggregator

agg = KnowledgeAggregator()
# One-shot: papers + Kaggle + vault combined
report = agg.search_all("feature engineering saturation", max_per_source=10)
# report.scholar_papers, report.arxiv_papers, report.kaggle_discs

Why This Matters

Real example: The feature-engineering-saturation-detection skill was created after seeing multiple Kaggle discussions warning about feature engineering saturation in tabular competitions — combining community signal with formal theory.

When to Apply

When to Use

• Starting any new Kaggle competition (read top solutions first)
• Looking for hyperparameter advice from competition winners
• Validating whether your CV score will translate to LB
• Finding "non-obvious" tricks (post-processing, ensemble weights, etc.)
• Comparing your approach to community consensus

When NOT to Use

• Pure research / academic projects (no Kaggle context)
• When network unavailable
• For deep technical theory (use arxiv instead)
• Privacy-sensitive contexts

Notes

• Sort by votes first — top-voted discussions are usually most informative
• Read comments, not just post — often the real insight is in comments
• Cross-check across competitions — same author posting on multiple competitions is a strong signal
• Combine with paper search — best insights usually combine theory (arxiv) + practice (Kaggle)
• Cache results in vault (importance=0.7+) for future reference
• See also: arxiv-paper-search, cv-lb-gap-acknowledgment, memory-hierarchy-management

References

• Implementation: framework/src/knowledge/kaggle_forum.py
• Aggregator: framework/src/knowledge/aggregator.py
• Kaggle discussions: https://www.kaggle.com/discussions
• Real skills enabled by Kaggle mining: feature-engineering-saturation-detection, cv-lb-gap-acknowledgment, ml-sweet-spot
• Related skill: arxiv-paper-search (complementary data source)