Row 9563

Row ID: 9563 | Dataset Entry | Axioma AXP Content Repository

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Hi, I have a tabular dataset, of which some are labelled and a large portion is unlabelled. I'm trying to minimize the log-loss on the unlabelled data so overfitting on it would be perfectly fine. What would be the best approach? I tried pseudo-labels (predicting the unlabelled data and adding the most confident samples to the training data) but it made almost no difference on the test loss.

Plus, I know the results (as the overall log-loss value) of a couple of predictions on this unlabelled dataset. Any way to utilize that?

FieldValue
text Hi, I have a tabular dataset, of which some are labelled and a large portion is unlabelled. I'm trying to minimize the log-loss on the unlabelled data so overfitting on it would be perfectly fine. What would be the best approach? I tried pseudo-labels (predicting the unlabelled data and adding the most confident samples to the training data) but it made almost no difference on the test loss. Plus, I know the results (as the overall log-loss value) of a couple of predictions on this unlabelled d…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-20
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Raw Record

{
  "text": "Hi, I have a tabular dataset, of which some are labelled and a large portion is unlabelled. I'm trying to minimize the log-loss on the unlabelled data so overfitting on it would be perfectly fine. What would be the best approach? I tried pseudo-labels (predicting the unlabelled data and adding the most confident samples to the training data) but it made almost no difference on the test loss.\n\nPlus, I know the results (as the overall log-loss value) of a couple of predictions on this unlabelled dataset. Any way to utilize that?",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-20",
  "username_encoded": "Z0FBQUFBQm5Lakw1ZlphWUFxREVoVUxNTk5NYlNFTnJERzU1V2hEYVhaNUNhelYzanI3SFI3OGhVeVdwT2hpOGRBZmo4OXBldlljS3pOMmg4cVdkVEpMYnprQm41VFZTZEE9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9JWlFiM1ZoWkxMTVkyNGlqa1EwSlNqckdjUk5KeG5ZWThMeWxiTGtPZEU3dlMwcUhCc0VPSmhlRl9SNXBnSDZWV2NhUWxOTjRkdU5XbjZNY3V5YjhWVDNDX2xEcFY3czZWWHJHQVVPckZrOWhwOTl3SGlNdDN3RHlyWE5ERk9QM3lzWElQT2pGLThBY2tGZ0w0Y1dyUXNzSG5wbVhOMFJYSmc3REE0NXNvX25tczVwajVveWVYdVlDdlpjek5aOTJG"
}
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