What does predictions
and label_ids
actually mean from Trainer.predict()
?
I trained a multilabel classification model and tested it on a test dataset. The Trainer.predict.metrics
gave me the output below:
I thought label_ids
should be the predicted label so I did a confusion matrix between label_ids
and my testing data. The result shows a perfect prediction with accuracy = 1, recall =1, precision = 1 etc.
I realized something was wrong so I computed the label myself with the logit values produced by Trainer.predict.predictions:
compute_labels = tf.round(tf.nn.sigmoid(test_prediction.predictions))
Running confusion matrix with the compute_labels
and the test data, I am able to get a reasonable prediction results that replicated the output of Trainer.predict.metrics
(i.e., above image).
My question is: What does Trainer.predict.label_ids
mean? Why the output I got from this argument produced a perfect prediction results which was obviously wrong?
Thank you in advance