dvcrn ML Evolution Agent 🤖
Auto-evolving ML competition agent that learns from every experiment.
What This Skill Does
- Auto-evolves ML models for Kaggle-style competitions
- HCC Multi-layer Memory - Episodic, Pattern, Knowledge, Strategic layers
- Continuous improvement - Each phase learns from previous failures/successes
- Resource-aware - Respects system limits (time, memory, API quotas)
When to Use
- User mentions Kaggle competition
- Tabular data classification/regression tasks
- Need to beat a target LB score
- User wants automated ML experimentation
Quick Start
# Initialize
from ml_evolution import MLEvolutionAgent
agent = MLEvolutionAgent(
competition="playground-series-s6e2",
target_lb=0.95400,
data_dir="./data"
)
# Run evolution
agent.evolve(max_phases=10)
HCC Memory Architecture
Layer 1: Episodic Memory
├── Experiment logs (phase, CV, LB, features, params)
├── Success/failure records
└── Resource usage tracking
Layer 2: Pattern Memory
├── What works (success patterns)
├── What fails (failure patterns)
└── When to use each approach
Layer 3: Knowledge Memory
├── Feature engineering techniques
├── Model configurations
├── Hyperparameter knowledge
└── Domain-specific features
Layer 4: Strategic Memory
├── Auto-evolution rules
├── Resource management rules
├── Exploration-exploitation balance
└── Competition-specific strategies
Proven Techniques (from real competitions)
Feature Engineering
| Technique | Effect | Best For |
|---|---|---|
| Target Statistics | +0.00018 LB | All tabular data |
| Frequency Encoding | +0.00005 LB | High-cardinality features |
| Smooth Target Encoding | +0.00003 LB | Prevent overfitting |
| Medical Indicators | +0.00006 CV | Health data |
Model Configurations
| Model | Best Params | Weight |
|---|---|---|
| CatBoost | iter=1000-1200, lr=0.04-0.05, depth=6-7 | 50% |
| XGBoost | n_est=1000-1200, lr=0.04, max_depth=6 | 25-30% |
| LightGBM | n_est=1000-1200, lr=0.04, leaves=40 | 20-25% |
Resource Limits
- Features: < 60 (avoids timeout)
- Iterations: < 1200 (avoids SIGKILL)
- Training time: < 20 min (system limit)
- Submissions: 10/day (Kaggle quota)
Evolution Rules
# Auto-evolution decision tree
if phase_improved:
keep_features()
try_similar_approach()
elif phase_degraded > 0.0001:
rollback()
try_new_direction()
else:
fine_tune_params()
# Overfitting detection
if cv_lb_gap > 0.002:
increase_regularization()
reduce_features()
simplify_model()
Files Structure
ml-evolution-agent/
├── SKILL.md # This file
├── HCC_MEMORY.md # Memory architecture details
├── FEATURE_ENGINEERING.md # Feature techniques library
├── MODEL_CONFIGS.md # Optimal model configurations
├── EVOLUTION_RULES.md # Auto-evolution decision rules
└── templates/
├── train_baseline.py # Baseline training script
├── train_evolved.py # Evolution training script
└── memory.json # Example memory state
Example Results
Playground S6E2 (Feb 2026)
- Started: LB 0.95347
- Best: LB 0.95365 (+0.00018)
- Phases: 14
- Success rate: 36%
- Target beaten: Yes (0.95361 → 0.95365)
Key Learnings
- Simple > Complex - Target stats beat complex feature engineering
- Resource limits matter - Too many features = timeout
- CatBoost is king - Consistently best for tabular data
- Daily quota awareness - Kaggle limits submissions
Installation
clawhub install ml-evolution-agent
Built from real competition experience. Evolved through 14 phases of experimentation.