Techniques For Feature Engineering To Improve Ml Model Accuracy

Abstract

In this paper, the author sought to understand the effects of feature engineering on the enhancements of the models learned by a machine learning algorithm. Feature engineering takes the raw data and prepares them for model inputs, increasing the model's effectiveness. Using different features on different datasets, this study assesses the performance of techniques like feature selection, feature extraction, feature scaling, feature engineering, and feature encoding. Using filter, wrapper, and embedded methods, we determine suitable features that describe a specific problem or situation well, while extraction methods such as PCA and autoencoders minimize feature dimensionality. Scaling techniques help normalize and scale the data, and the encoding methods assist in translating a categorical variable to a numerical value. As can be seen from the results, there are substantial enhancements in model performance, stability, and training time. Examples in finance, healthcare, and e-commerce are highlighted to show how these approaches solve diverse problems, such as detecting fraud, predicting diseases, or segmenting customers. There are also examples of feature selection and evaluation problems and their solutions discussed in the paper, which include issues with dimensionality and multicollinearity. In this respect, the study aims to discuss these challenges and recommend how feature engineering can be integrated to improve model performance and interpretability in real-world cases.

Published Date: 3 February 2021