I have trained a neural network multi-class classification model with around 150 classes having around 85% accuracy. Once the model is trained and deployed, it's predicting on new data and I am saving the logs. Now I have to detect those data-points which is wrongly predicted by the model. For example, The model predicted on 10 data-points, out of which there might be 3 data-points which are wrongly classified by the model. Is there any way to get those data-points? I have the following data.
- The deployed model.
- The data-points.
- Corresponding predicted classes by the model.
- Confidence scores on the prediction.
I know that using confidence score I might get some idea on wrong prediction, but I am thinking is there any other way to get that?