Collecting more labelled data takes time. Before requesting another large batch, a team can ask how the current model’s performance changes when it learns from progressively larger subsets of the available training data.
That is the question behind a learning curve. The scikit-learn documentation describes plotting training and validation scores against the number of training samples. This differs from plotting loss against training epochs: the horizontal axis answers a different question.
Keep the comparison controlled
Use a suitable evaluation design, consistent scoring and a defined model configuration. Preprocessing must remain inside the appropriate training process so information from validation examples does not leak into fitting. Time-dependent or grouped records need a split that respects those relationships.
Record how subsets are selected and show variability across the chosen splits where possible. A smooth-looking line made from one lucky sample can conceal the uncertainty that matters to the next decision.
Read patterns as clues
If training performance is strong while validation performance is weaker, and the gap narrows as data increases, additional representative data may be worth investigating. If both scores remain disappointing and close together, simply collecting more of the same examples may not address the main limitation.
These are diagnostic clues, not automatic prescriptions. Label quality, missing features, distribution mismatch and the evaluation metric can all affect interpretation. A low score under one metric may also hide different kinds of failure that need separate examination.
Turn the plot into a bounded experiment
For an illustrative document classifier, the next step might be to label a modest additional set from an underrepresented document type, then evaluate it under the same protocol. Another result might justify revisiting the feature representation first. Neither decision requires pretending the curve predicts an exact future accuracy.
Keep a final test set for its intended role rather than repeatedly tuning decisions against it. Document the curve’s data source, sample sizes, metric and model settings beside the resulting recommendation. The useful output is a reasoned next experiment with its assumptions visible, not a promise that one more data purchase will solve the model.