Update README.md

README.md

@@ -109,7 +109,7 @@ The MCC, a balanced metric which takes into account true and false positives and
 - **Improvement Suggestions**:
   - **Precision Improvement**: Focus on strategies to improve precision, such as feature engineering or experimenting with different classification thresholds.
   - **Hyperparameter Tuning**: Engaging in hyperparameter tuning might assist in enhancing the model's performance on unseen data.
-  - **Complexity Reduction**: Consider reducing the model's complexity to prevent potential overfitting and improve generalization.
+  - **Complexity Reduction**: Consider reducing the model's complexity by training a LoRA for different weight matrices to prevent potential overfitting and improve generalization.
   - **Class Imbalance**: If the dataset has a class imbalance, techniques such as resampling or utilizing class weights might be beneficial.
 
 In conclusion, the model performs well on the training dataset and maintains a reasonably good performance on the test dataset, demonstrating a solid generalization capability. However, the decrease in certain metrics like precision and F1-score in the test dataset compared to the training dataset indicates room for improvement to optimize the model further for unseen data. It would be advantageous to enhance precision without significantly compromising recall to achieve a more harmonious balance between the two.