Feature Engineering Strategies for Improving Predictive Modeling in Sparse Data Environments

Abstract

Predictive modeling under sparse data conditions remains one of the most significant challenges in machine learning. Sparse datasets frequently arise in domains characterized by rare events, limited observations, or highly dimensional inputs with minimal support across features. This paper provides a comprehensive analysis of feature engineering techniques designed to address sparsity by leveraging dimensionality reduction, domain-driven synthesis, embedding-based transformations, and multi-resolution aggregation. Grounded in the broader AI research literature from 2017 to 2019, the study evaluates strategies that enhance predictive stability, reduce overfitting, and maintain representational richness. Conceptual visualizations and compara tive tables illustrate the behavior of candidate methods in sparse environments. The findings emphasize the importance of structural inductive bias, domain knowledge, and computational efficiency in constructing meaningful features for real-world predictive tasks.