There are several evaluation metrics commonly used to assess the performance of text classification models. The choice of metric depends on the specific goal and requirements of the classification task. Here are some commonly used metrics:

  1. Accuracy: Accuracy measures the overall correctness of the model by calculating the ratio of correct predictions to the total number of predictions. It is useful when the class distribution is balanced.

  2. Precision: Precision calculates the ratio of true positives (correctly predicted positive samples) to the total number of predicted positives. It measures how well the model correctly identifies positive instances.

  3. Recall: Recall calculates the ratio of true positives to the total number of actual positives. It measures how well the model retrieves positive instances.

  4. F1-score: The F1-score is the harmonic mean of precision and recall. It provides a balanced measure of precision and recall and is useful when the class distribution is imbalanced.

  5. Area Under the Receiver Operating Characteristic curve (AUC-ROC): ROC curve plots the true positive rate against the false positive rate at various classification thresholds. AUC-ROC measures the model's ability to distinguish between positive and negative instances.

  6. Confusion Matrix: A confusion matrix provides a detailed breakdown of the model's predictions by showing the true and false positives and negatives. It gives insights into the model's performance on each class.

  7. Classification Report: A classification report provides the precision, recall, F1-score, and support for each class in the dataset. It provides a comprehensive evaluation of the model's performance on individual classes.

  It is important to consider which metric is most appropriate based on the specific requirements and characteristics of the text classification task. Different evaluation metrics can give different insights into the model's performance.