DOCUMENTATION

Score a new batch of data

You have a trained model and a file of rows to predict. Here is the gesture, and what LabML checks before accepting.

This assumes you have already trained a model. If not, start with the tutorial.

The gesture

  1. Below the leaderboard, open Score a new batch.
  2. Drop the file — CSV, TSV or Excel.
  3. Predictions appear row by row, exportable as CSV.

Train iris to try it with iris-field.csv

## What LabML checks before accepting

The file must carry the columns the model expects. If any are missing, LabML refuses and names them (missing-columns) rather than predicting over absent features.

Extra columns are ignored without noise: a production export often carries identifiers the model never needed.

If the file contains the real answer

Then LabML does not merely predict: it compares, and shows the same metrics as the leaderboard.

This is the most useful production gesture — checking that a model still holds on data it has never seen. A clear gap against the test score is a drift signal, to investigate in the Data Studio.

The decision columns

If the scored model carries a multiclass decision rule — frozen in the lab, then exported with it — the produced CSV gains two columns:

Column Contents
policy_decision The class the rule settled on, or an empty cell when the rule abstained.
decision_status decided or abstained.

The predicted column stays the model's raw prediction. The two decision columns do not replace it: they say what the rule chose to do with it. An abstained row is a case to route to human review, not a model error.

With no rule attached to the model, these columns do not appear at all.

Labels never seen

If your batch contains a class absent from training, LabML still predicts it but excludes it from the metrics, announcing how many rows are affected. The model cannot predict a class it never met; counting it in accuracy would distort it.

Where to go next