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Absolutely! Understanding the roles of training, validation, and test sets is essential for building and evaluating machine learning models effectively.
📂 1. Training Set
- Purpose: Used to teach the model.
- Contains: Input data + correct output labels.
- What happens: The model learns patterns and adjusts its parameters using this data.
🧪 2. Validation Set
- Purpose: Used to tune hyperparameters and check the model’s performance during training.
- Contains: Data the model hasn’t seen during training.
- What happens: Helps detect overfitting or underfitting.
- Example use: Choosing the number of layers in a neural network or deciding when to stop training (early stopping).
✅ 3. Test Set
- Purpose: Used only at the very end to evaluate the final model’s performance.
- Contains: Completely unseen data.
- What happens: Measures how well the model generalizes to real-world data.
🎯 Summary Table:
| Set | Used For | When Used |
|---|---|---|
| Training | Learning patterns | During training |
| Validation | Model tuning & evaluation | During training |
| Test | Final performance check | After training |
Would you like a visual flowchart showing how these sets are used in the ML workflow?