In the realm of machine learning (ML), label errors denote incorrect labels assigned to instances within a dataset. These errors can stem from multiple factors, including human annotation mistakes, misclassification, or data corruption.
Label errors can profoundly affect the performance of an ML model, especially when the errors are systematic or concentrated in specific classes or regions of the feature space. For instance, if a dataset has numerous label errors for a particular class, the model might struggle to learn the accurate decision boundary for that class, resulting in subpar performance.
To address the issue of label errors in ML, various strategies can be employed. One approach is to estimate the model's generalization error through techniques such as cross - validation or bootstrapping. This can help identify cases where the model is overfitting to the training data due to label errors.
Another strategy involves rectifying or enhancing the labels in the dataset using methods like active learning or self-training. With these techniques, the model is iteratively trained on a subset of the data, and its predictions are then utilized to detect and correct label issues in the remaining data instances.
Overall, handling label errors can be challenging during the development of machine learning models. However, by applying appropriate methods and procedures, it is possible to develop models that are robust against such errors.