dimensionality reduction

This tag covers discussions and articles on dimensionality reduction techniques applied to text classification and machine learning, particularly in the context of Lithuanian language data. Topics include the use of generative AI for data augmentation, preprocessing strategies, and benchmarking traditional models like SVM and logistic regression. Dimensionality reduction methods such as PCA and feature selection are explored to improve model performance and handle high-dimensional sparse text data. The content emphasizes practical applications in educational settings with limited datasets.