The approach of exploratory data analysis (EDA) is commonly used to uncover a parsimonious model in data analysis because it emphasizes understanding the underlying structure and relationships within the data before moving to more complex modeling techniques. EDA involves visually and statistically examining the data to identify patterns, trends, and anomalies. By doing this, analysts can better understand which variables or features are truly impactful and which ones may be redundant or irrelevant.
This thorough examination allows practitioners to streamline the model by focusing on a simpler set of predictors that still effectively explain the variability in the response, fostering a balance between model simplicity and predictive performance. The goal of a parsimonious model is to achieve high interpretability and efficiency while minimizing overfitting, which EDA facilitates by promoting careful consideration of the significant features.
While data visualization techniques and feature engineering processes can contribute to the understanding of data, they do not inherently focus on simplifying the model based on the exploratory insights. Model ensemble methods, on the other hand, tend to combine multiple models rather than striving for parsimonious solutions. Therefore, EDA stands out as the primary approach conducive to identifying a simpler yet effective model framework.