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August 17, 2025

Identifying Optimal Data Distributions for Enhanced Data Modeling in Machine Learning

Key Points

Identifying the optimal data distribution can significantly enhance model accuracy and effectiveness in machine learning projects.
Key techniques include visual representations like histograms and statistical tests such as the Kolmogorov-Smirnov test and Anderson-Darling test.
Model evaluation criteria such as Akaike information criterion and Bayesian information criterion are essential for ensuring a good fit.
Understanding data distribution helps prevent poor model performance, as illustrated by the California Housing dataset.

Abstract

Understanding how data is distributed is crucial for building accurate models in machine learning and data science projects. In this paper, we explore practical methods to help identify the best-fitting distribution for real-world datasets. We cover visual techniques like histograms and Q-Q plots, as well as statistical tests such as Kolmogorov-Smirnov (KS) and Anderson-Darling (AD). We also look at model evaluation using criteria like Akaike information criterion (AIC) and Bayesian information criterion (BIC) to ensure a good fit. To illustrate these methods, we use the California Housing dataset, showing how wrong assumptions about data distribution can lead to poor model performance. By following the guidelines provided in this paper, data scientists can choose the right distribution, leading to more accurate models, better anomaly detection, and smarter decisionmaking across different fields.

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