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What is ? What Is A Confusion Matrix about?
A Confusion Matrix is a tool used to evaluate the performance of a classification model by comparing actual and predicted values. It consists of four components: True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN), which help in calculating performance metrics like accuracy, precision, recall, and F1-score. Understanding these metrics is crucial for assessing model effectiveness and identifying the types of errors made.
- Author
- Sanjana Chaudhari
- Language
- EN