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ML models show inconsistent feature importance in electrospinning research

By PulseAugur Editorial · [2 sources] ·

A new research paper explores the consistency of feature importance across various machine learning models in the context of electrospinning. The study evaluated 21 different ML models using SHAP values to assess the reliability of parameter rankings. Findings indicate that while predictive accuracy can be similar, feature importance can vary significantly between models, suggesting that relying on a single model for interpretation may be misleading, especially with limited experimental data. AI

IMPACT Highlights the need for cross-model validation in ML interpretability to ensure reliable insights, particularly in scientific research.

RANK_REASON Academic paper on machine learning interpretability methods.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

ML models show inconsistent feature importance in electrospinning research

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Academic paper on machine learning interpretability methods.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Mehrab Mahdian, Ferenc Ender, Tamas Pardy ·

    Cross-Model Consistency of Feature Importance in Electrospinning: Separating Robust from Model-Dependent Features

    arXiv:2605.04905v1 Announce Type: new Abstract: Electrospinning is a highly sensitive fabrication process in which small variations in operating parameters can significantly influence fiber morphology and material performance. Machine learning (ML) methods are increasingly employ…

  2. arXiv cs.LG TIER_1 English(EN) · Tamas Pardy ·

    Cross-Model Consistency of Feature Importance in Electrospinning: Separating Robust from Model-Dependent Features

    Electrospinning is a highly sensitive fabrication process in which small variations in operating parameters can significantly influence fiber morphology and material performance. Machine learning (ML) methods are increasingly employed to model these process-structure relationship…

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