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Recently, the selection of machine learning model based on only the datadistribution without concerning the noise of the data. This study aims todistinguish, which models perform well under noisy data, and establish whetherstacking machine learning models actually provide robustness to otherwiseweak-to-noise models. The electrochemical data were tested with 12 standalonemodels and stacking model. This includes XGB, LGBM, RF, GB, ADA, NN, ELAS,LASS, RIDGE, SVM, KNN, DT, and the stacking model. It is found that linearmodels handle noise well with the average error of (slope) to 1.75 F g-1 up toerror per 100% percent noise added; but it suffers from prediction accuracy dueto having an average of 60.19 F g-1 estimated at minimal error at 0% noiseadded. Tree-based models fail in terms of noise handling (average slope is55.24 F g-1 at 100% percent noise), but it can provide higher predictionaccuracy (lowest error of 23.9 F g-1) than that of linear. To address thecontroversial between prediction accuracy and error handling, the stackingmodel was constructed, which is not only show high accuracy (intercept of 25.03F g-1), but it also exhibits good noise handling (slope of 43.58 F g-1), makingstacking models a relatively low risk and viable choice for beginner andexperienced machine learning research in electrochemistry. Even though neuralnetworks (NN) are gaining popularity in the electrochemistry field. However,this study presents that NN is not suitable for electrochemical data, andimproper tuning resulting in a model that is susceptible to noise. Thus, STACKmodels should provide better benefits in that even with untuned base models,they can achieve an accurate and noise-tolerant model. Overall, this workprovides insight into machine learning model selection for electrochemicaldata, which should aid the understanding of data science in chemistry context.

How False Data Affects Machine Learning Models in Electrochemistry?