Improve your classifier

If you want to improve your classifier, it is best to use the Confusion Matrix. The fields marked in shades of orange show you the categories for which a high number of your examples could not be classified correctly. This is where you can start to improve the data quality.

By clicking on such a field, the examples contained in it open below the Confusion Matrix. These can be edited there directly.

In total, there are 4 cases of improving the classifier:

  1. The classifier is wrong: If the classifier is wrong and the label and the text example go together correctly, you do not have to change anything in your examples.
  2. The label is wrong: It is possible that an error has crept into your examples with the label. Please read the text examples again and correct the label if necessary.
  3. The label is correct, the classifier is also correct: Split your text sample into two examples and assign them to the appropriate categories.
  4. The label is wrong, the classifier is also wrong: Correct the label so that your classifier learns the correct assignment during the next training.

If there is a lot of confusion between thematically similar categories, it is recommended to merge the two categories. After you have made the adjustments, start a new training session.

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