Feature Engineering and Data Visualization Analysis in Artificial Intelligence in Big Data Era

Abstract

In the environment of massive data, the selection and construction of feature engineering plays a crucial role in the performance and accuracy of sgon models. It is true that the classic hand-driven feature building method can incorporate insights from the professional field, but this method is potentially accompanied by the hidden trouble of information omission, and does not necessarily touch the boundary of the optimal solution. In order to solve these problems, this paper proposes two strategies of feature extraction: ensemble learning and deep learning. Ensemble learning enhances generalization by combining the opinions of multiple models, while deep learning allows models to automatically learn features, reducing the need for human intervention. Both of these methods can overcome the limitations of manual feature design to varying degrees. In addition, the paper also introduces the application of parallel coordinate graph in feature selection. By using the parallel axis system to implement projection transformation of high-dimensional data, scholars can intuitively analyze the data structure, so as to promote the process of feature selection and optimization. This method not only gives insight into the subtle relationship between the data, but also cleverly stimulates the potential of human pattern recognition and further improves the comprehensive performance of the model.