Feature Engineering and Selection Strategies for Improving Machine Learning Accuracy

Abstract

A significant contribution to the enhancement of the precision and dependability of machine learning models is made by the processes of feature engineering and feature selection. When dealing with complicated and high-dimensional datasets, the quality of the input features frequently has a higher impact on the performance of the model than the algorithm that is selected itself. Feature engineering is the process of transforming raw data into informative representations, whereas feature selection is the process of identifying the variables that are most significant, hence eliminating noise and repeated information. These techniques for feature engineering include normalization, encoding, feature building, and domain-driven transformations. Additionally, these techniques include feature selection strategies such as filter, wrapper, and embedding methods. This article will discuss how these approaches improve model generalization, decrease overfitting, and enhance computing efficiency across a variety of machine learning problems. An analysis of the impact of feature engineering and selection on both traditional and advanced learning models is presented, along with comparative observations for each.