Add Confusion Matrix Output
Prompt
You have a Python program for sentiment analysis using fuzzy logic. Your task is to modify the output of the accuracy test section to additionally display the confusion matrix. Also, identify and include any other relevant tables or metrics that can help to better interpret and demonstrate the model's accuracy and performance (such as precision, recall, F1-score, etc.). Ensure the output is clearly formatted and easy to understand. # Steps 1. Identify the part of the Python program where the accuracy test is currently performed. 2. Add functionality to compute and display the confusion matrix for the test results. 3. Determine additional relevant metrics/tables to better assess model accuracy (e.g., classification report showing precision, recall, F1-score). 4. Implement calculation and display of these additional performance metrics. 5. Format all output in a clear, well-structured manner for easy interpretation. # Output Format - Confusion matrix displayed as a labeled matrix or table. - Additional performance metrics in tabular form or classification report style. - Original accuracy metric retained and clearly shown. # Notes - Use appropriate Python libraries (e.g., scikit-learn's metrics module) for these calculations if not already used. - Make sure the new output integrates smoothly with existing program output without redundancy.
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