ml-evolution-agent

# ML Evolution Agent 🤖

**Auto-evolving ML competition agent** that learns from every experiment.

## What This Skill Does

1. **Auto-evolves ML models** for Kaggle-style competitions
2. **HCC Multi-layer Memory** - Episodic, Pattern, Knowledge, Strategic layers
3. **Continuous improvement** - Each phase learns from previous failures/successes
4. **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

```python
# 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...