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Model Interpretability in ML: Bridging Performance and Trust

Abstract The increasing deployment of machine learning (ML) systems in high-stakes applications has amplified the demand for model interpretability.

Introduction As machine learning (ML) and artificial intelligence (AI) systems become increasingly integrated into daily life—from loan approva

Introduction Resampling techniques are one of the most effective ways to handle class imbalance in machine learning. These methods modify the dataset

Introduction Data preprocessing is a crucial step in handling imbalanced data in machine learning. It involves transforming raw data into a format th

Introduction In machine learning, imbalanced data refers to datasets where one class significantly outnumbers another. This is common in real-world a

Introduction Class imbalance is a common challenge in machine learning, particularly in classification problems where one class significantly outnumb

Introduction Neural implants are advanced biomedical devices designed to interact directly with the brain or nervous system. These implants can resto

Introduction Virtual Reality (VR) is a revolutionary technology that immerses users in a computer-generated environment, enabling them to interact wi

Introduction Neuroprosthetics is a groundbreaking field that integrates neuroscience and engineering to create prosthetic devices controlled by the b

Introduction Electroencephalography (EEG) is a non-invasive technique used to record electrical activity in the brain. By placing electrodes on the s