kaggle-discussion-search

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