lipu-jpg Paper Experiment
Execute experiments, analyze results, and validate findings with statistical rigor.
Experiment Workflow
- Design → Define hypotheses, metrics, baselines
- Execute → Run experiments with logging
- Analyze → Statistical tests, visualization
- Validate → Reproduce key results, check robustness
Metrics Selection
Common ML Metrics
| Task | Metrics |
|---|---|
| Classification | Accuracy, F1, AUC, Precision, Recall |
| Generation | BLEU, ROUGE, METEOR, BERTScore |
| QA | EM, F1 |
| Regression | MSE, MAE, R² |
Statistical Tests
- t-test: Compare two means
- ANOVA: Compare multiple groups
- Wilcoxon: Paired non-parametric
- Bonferroni: Multiple comparison correction
Results Analysis
Visualization
- Learning curves (train/val over epochs)
- Bar charts for comparative results
- Confidence intervals / error bars
- Confusion matrices
Tables
- Main results with statistical significance (* p<0.05)
- Ablation studies
- Hyperparameter sensitivity
- Runtime / resource comparisons
Scripts
experiment-runner.py
- Grid search and random search
- Parallel experiment execution
- Checkpointing and resume
results-analyzer.py
- Auto-generate tables and figures
- Statistical significance testing
- Result aggregation from multiple runs
Tips
- Run baseline first to establish comparison
- Save all intermediate results, not just final
- Check for statistical significance, not just improvement magnitude
- Document any unexpected observations