How to Deal With Missing Values in Predicted Data in Confusion Matrix

After a model has predicted results, for example in object detection, some detections are missed. A saved model will therefore have a different shape (prediction values) compared with the ground truths (target/expected). That is, ground truth values will be more than the predicted values. Using confusion matrix such as below, How do you handle this to avoid discrepancy in classification report while maintaining the same shape for both y_trues and y_preds?


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