In the realm of data analysis, medium data sets—those too large for basic statistics yet too small for robust machine learning—often suffer from overfitting, with 75% of such sets showing this issue. Overfitting occurs when models fail to capture true variability, leading to unreliable predictions. To address this, a novel approach using elasticity as a predictor has been developed. This method involves calculating an elasticity constant by dividing the percentage change of inputs by the percentage change of outputs. This constant then helps predict output changes based on known input changes. While this technique offers insights into overall dataset impacts, it should be approached cautiously. Stakeholders are advised to use this method with caution, understanding its limitations and the potential for larger effects when specific inputs are altered, based on past observations. This approach exemplifies how basic statistics can be integrated into models to support data-driven decisions in medium data scenarios.
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