We found some data slices in your dataset containing significant number of underconfident predictions. Underconfident predictions refer to situations where the predicted label has a probability that is very close to the probability of the next highest probability label. This happens when:

• There are not enough examples in the training set for the underconfident data slice
• The model is too simple and struggles to capture the complexity of the underlying data
• The underconfident data slice contains inherent noise or overlapping feature distributions

To learn more about causes and solutions, check our guide on underconfidence issues.

We found some data slices in your dataset on which your model performance is lower than average. Performance bias may happen for different reasons:

• Not enough examples in the low-performing data slice in the training set
• Wrong labels in the training set in the low-performing data slice
• Drift between your training set and test set

To learn more about causes and solutions, check our guide on performance bias.

Your model seems to be sensitive to small perturbations in the input data. These perturbations can include adding typos, changing word order, or turning text into uppercase or lowercase. This happens when:

• There is not enough diversity in the training data
• Overreliance on spurious correlations like the presence of specific word
• Use of complex models with large number of parameters that tend to overfit the training data

To learn more about causes and solutions, check our guide on robustness issues.