Testing unlabeled data on a trained model
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Dear Matlab community,
I need to know if there's a way to test the reliability of predictions made by classifying new data (unlabeled data) using and already trained model.
This is what I did:
1) Create a dataset with labeled data, with 2 predictors and 3 response variables (training set);
2) Fit and validate a Multiclass Support Vector Machine classifier using the training set;
3) Use the obtained model to make predictions on a new dataset with unlabeled data (test set)
I would like to know which are the classification metrics (if there are) to establish the relaibility of this classification, since the new data is unlabeled.
Thanks.
4 Comments
Tarunbir Gambhir
on 29 Oct 2020
If your labeled training data and the unlabeled test data have a high correlation, the best thing you can do is to use a small partition of the labeled training data as test data to get a quantitative measure on reliability. The high correlation should ensure similar performance with your unlabeled test data.
Apart from this, I don't think there is any reliable way to get performance of your model on real data without ground truth.
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