Instance Hardness

Instance hardness refers to the varying difficulty of correctly classifying or predicting the outcome for individual data points within a dataset. Current research focuses on identifying and characterizing these hard instances across diverse machine learning tasks, developing methods to quantify instance difficulty (e.g., using meta-features or learned representations), and leveraging this information to improve model training and evaluation (e.g., through weighted sampling, early exiting strategies, or adaptive algorithms). Understanding and addressing instance hardness is crucial for enhancing model performance, improving data quality, and developing more efficient and reliable machine learning systems across various applications.