Classification Datasets

Classification datasets are crucial for training and evaluating machine learning models, with current research focusing on mitigating dataset bias, improving model interpretability, and optimizing model selection processes. Studies explore various model architectures, including random forests, deep learning approaches, and novel algorithms tailored to specific data types like time series and tabular data. This research is vital for enhancing the reliability and trustworthiness of machine learning systems across diverse applications, from medical diagnosis to autonomous driving, by addressing issues like dataset bias and improving model explainability.