# Error Handling Reference

Common issues and solutions:

**Insufficient Training Time**
- Error: AutoML search terminated before finding good model
- Solution: Increase time budget, reduce search space, or use faster algorithms

**Memory Exhaustion**
- Error: Out of memory during pipeline training
- Solution: Reduce dataset size through sampling, use incremental learning, or simplify feature engineering

**Poor Model Performance**
- Error: Best model accuracy below acceptable threshold
- Solution: Collect more data, engineer better features, expand algorithm search space, or adjust evaluation metrics

**Feature Engineering Failures**
- Error: Automated feature transformations produce invalid values
- Solution: Add data validation checks, handle missing values explicitly, restrict transformation types

**Model Convergence Issues**
- Error: Optimization fails to converge for certain algorithms
- Solution: Adjust hyperparameter ranges, increase iteration limits, or exclude problematic algorithms