Reevaluating feature selection in phase field models for battery performance: A call for robust statistical approaches
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OpenAlex topics: Advanced Battery Technologies Research · Fault Detection and Control Systems
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References (9)
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1. Combination of high-throughput phase field modeling and machine learning to study the performance evolution during lithium battery cycling
2. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation10.7717/peerj-cs.623 ↗
3. Relationship between prediction accuracy and feature importance reliability: an empirical and theoretical study10.1016/j.neuroimage.2023.120115 ↗
4. The bias of the lasso and worst possible sub-directions
5. Omitted variable bias of lasso-based inference methods: a finite sample analysis10.1162/rest_a_01128 ↗
6. A robust Spearman correlation coefficient permutation test10.1080/03610926.2022.2121144 ↗
7. Nonparametric estimation of Spearman's rank correlation with bivariate survival data10.1111/biom.13453 ↗
8. Correlation tests in R: Pearson cor, Kendall's Tau, and Spearman's Rho
9. On attainability of Kendall's tau matrices and concordance signatures10.1016/j.jmva.2022.105033 ↗