Description: Label Noise refers to errors in the labeling of training data that can negatively affect the performance of machine learning models. This phenomenon occurs when the labels assigned to the data do not accurately reflect reality, which can be due to various reasons such as human errors, ambiguity in the data, or inconsistencies in labeling criteria. Label noise is a significant challenge in the field of supervised learning, where the quality of training data is crucial for the model’s success. When a model is trained with poorly labeled data, it may learn incorrect patterns, resulting in poor performance in classification or prediction tasks. Additionally, label noise can lead to greater variability in results, making it difficult for the model to generalize to new data. Therefore, it is essential to implement data cleaning and validation strategies to mitigate the impact of label noise and improve the robustness of machine learning models.
