Comparison of Feature Selection Methods on High-Dimensional Dummy Data

Feature selection is a crucial step in data analysis, particularly when dealing with high-dimensional datasets. This study compares the performance of various feature selection methods on high-dimensional dummy data. By generating synthetic datasets with different feature dimensions and levels of redundancy, the research evaluates the effectiveness of methods such as correlation-based feature selection, information gain, and recursive feature elimination. The evaluation criteria include accuracy, computational efficiency, and interpretability. The findings contribute to the understanding of the suitability of different feature selection methods for high-dimensional dummy data, providing valuable guidance for researchers and practitioners working with complex datasets.