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Update finetune.md #7

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13 changes: 2 additions & 11 deletions docs/Advanced/finetune.md
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Expand Up @@ -12,7 +12,8 @@ Our baseline model provides robust performance across many use cases. However, f
- Start by testing the baseline model on a representative subset of your data using the [Evaluation function](#performance-evaluation)
- Collect performance metrics:
- Accuracy rate
- Cohen’s Kappa
- F1 Score
- Recall

2. Decision Criteria for Fine-Tuning
- Consider fine-tuning if:
Expand Down Expand Up @@ -50,16 +51,6 @@ You can evaluate model performance using the `CALL MODEL_OPTIMIZATION.EVALUATE_P
CALL MODEL_OPTIMIZATION.EVALUATE_PERFORMANCE();
```

### **Interpretation of Kappa Score:**
| **Kappa Score (κ)** | **Level of Agreement** |
|----------------------|------------------------|
| < 0.0 | Poor (Worse than chance) |
| 0.0 – 0.20 | Slight agreement |
| 0.21 – 0.40 | Fair agreement |
| 0.41 – 0.60 | Moderate agreement |
| 0.61 – 0.80 | Substantial agreement |
| 0.81 – 1.00 | Almost perfect agreement |

# Fine-tuning the Model

If you want to increase the accuracy by tuning the model to your own network you can use the following commands.
Expand Down