Introduction | Installation and Setup | Basic Usage | Creating Sample Data | Basic Evaluation | Evaluation with Probabilities | Understanding the Metrics | Classification Metrics | Probabilistic Metrics | Inter-rater Reliability | Visualization | Integration with transforEmotion Workflow | Complete Pipeline Example | Using with CSV Data | Advanced Usage | Custom Metrics Selection | Handling Missing Data | Custom Column Names | Best Practices | 1. Always Include Probability Scores | 2. Use Appropriate Metrics | 3. Validate Data Quality | 4. Report Multiple Metrics | Conclusion

1. Python setup using setup_miniconda() | 2. Using transformer_scores | 3. Using image_scores | 4. Using `video_scores'