Intelligent Feature Engineering and Feature Selection Techniques for Machine Learning Evaluation

Manual feature engineering can take a long time and be ineffective at capturing complicated patterns, while choosing the wrong features can produce less-than-ideal outcomes. As a result, effective feature engineering and selection strategies are crucial for enhancing machine learning evaluation. To improve the assessment of machine learning algorithms, we suggest intelligent feature engineering and feature selection strategies in this study. The two key phases of our strategy are feature engineering and feature selection. We use cutting-edge techniques like deep learning and autoencoders for feature engineering to automatically extract pertinent representations from raw data. High-level characteristics that represent intricate linkages and buried patterns can be extracted using these techniques. We use sophisticated algorithms, such as statistical methods, evolutionary algorithms, and correlation analysis, during the feature selection step to find the most informative features while minimizing dimensionality. Results from experiments show that models created with our intelligent feature engineering and selection strategies perform better than models created using more conventional methods, with an MSE value of 0.0202920. We intend to investigate novel deep learning architectures created expressly for feature engineering tasks in upcoming research. We also intend to research dynamic feature set adaptation techniques for feature selection based on reinforcement learning. For further improvement, ensemble methods integrating various feature engineering and selection strategies will be investigated. Additionally, we stress the necessity of standardized benchmarks and evaluation procedures to enable fair comparisons between various feature engineering and selection methods and to promote improvements in the field.