> For the complete documentation index, see [llms.txt](https://neurablock.gitbook.io/whitepaper/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://neurablock.gitbook.io/whitepaper/machine-learning-model/training-validation-and-testing-of-the-model.md).
# Training, Validation, and Testing of the Model
* **Training:** The model is trained using a dataset of contracts with known outcomes (malicious or benign). This phase involves adjusting the model's parameters to minimize error and improve predictive accuracy. (Forta Dataset)
Current results:
Precision: 63% Recall: 79% F1-Score: 70%
* **Validation:** A separate dataset is used for validation. Techniques like cross-validation help in tuning the model and selecting the best version to prevent overfitting or underfitting.
* **Testing:** The final step involves evaluating the model's performance on a test dataset that it has never seen before. This provides an unbiased assessment of its predictive capabilities.
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