Model Evaluation

For evaluating text classification tasks, it's generally better to use metrics such as precision , recall, and AU - ROC rather than relying solely on accuracy. These metrics provide a more detailed understanding of model performance, especially when the dataset is imbalanced. While accuracy measures overall correctness, precision and recall help assess how well the model identifies each class, and AU - ROC captures the model's ability to distinguish between positive and negative classes across different thresholds.