Dimentionality Reduction in Machine Learning

This study investigates the efficency of linear discriminant analysis (LDA) and principal component analysis (PCA) as dimensionality reduction methods to enhance machine learning performance and lessen overfitting. Whereas LDA, a supervised technique, improves class separability by optimizing the ratio of between-class to within-class variance, PCA, an unsupervised technique, simplifies data by concentrating on variance. In order to overcome the drawbacks of applying LDA directly to highdimensional data—especially for classification tasks—the study integrates these techniques. The results show that feature extraction, classification accuracy, and model generalization are greatly enhanced by PCA and LDA, both separately and in combination.