Automated Feature Engineering in Machine Learning: Challenges and Innovations
Keywords:
Automated Feature Engineering, Machine Learning, Leveraging AI, Creation, Improving Efficiency, Model Performance, Reducing Human Bias, AutoML Tools, Deep Learning.Abstract
This research explores the emerging field of automated feature engineering in machine learning and stresses how machine learning and AI algorithms can potentially revolutionize predictive modeling. Automation will ease feature generation, transformation, and selection, making it more efficient, boosting model performance, and reducing human bias. Feature engineering has traditionally been time-consuming and skill-based. The research examines several AI-based approaches, i.e., AutoML tools, feature extraction using deep learning, and feature selection using reinforcement learning. It also addresses problems like overfitting, scalability, and the complexity of preprocessing data. The presentation of case studies and applications focuses on the rising importance of machine learning automation. Innovations for future developments and trends in automatic feature engineering are also presented in the conclusion of the paper as informative perspectives to practitioners and researchers.
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