Good results from sensor data: Performance of machine learning algorithms for regression problems in chemical sensors

Accurately predicting unseen data, instead of mere memorization of training examples, is a critical goal of machine learning. This generalization is particularly important in the field of chemical sensors, where the ability to accurately predict the chemical properties or concentration levels of unknown samples is crucial. The paper presents a comprehensive yet accessible introduction to various machine learning concepts, highlighting the importance of model interpretability and generalization in ensuring reliable and accurate results in this context. Nonlinear sensor array data are utilized to introduce key concepts (e.g., bias-variance tradeoff) and techniques (linear models, partial least squares regression, support vector machines, k -nearest neighbors, decision trees, ensemble methods, automated machine learning, symbolic regression, and artificial neural networks), providing a solid foundation to make informed decisions when selecting machine learning techniques for sensor-specific regression applications. The results clearly indicate a number of conclusions. First, overparameterized deep feedforward neural networks show great accuracy and generalization when trained on a sufficiently large dataset. Second, symbolic regression models proved to be more accurate than deep feedforward neural networks and classical machine learning techniques on smaller datasets. Third, the performance of various machine learning models was dataset-dependent, showing the importance of comparative studies to determine the most suitable approach. It is clear that the optimal model cannot be known a priori . This paper aims to provide a starting point for investigations on the performance of different machine learning techniques in chemical sensor applications. • The study found that deep feedforward neural networks and symbolic regression models outperformed classical machine learning techniques in handling complex relationships within nonlinear sensor array datasets. • Overparameterized deep feedforward neural networks demonstrated excellent accuracy and generalizability when trained on large sensor datasets, in line with recent theoretical machine learning findings. • When dealing with small datasets (<9000 training examples), symbolic regression models were shown to be the most effective in predicting the concentrations of unknown samples. • The research highlighted the limitations of combining sensors with different selectivity patterns. Sensor arrays were unable to achieve accurate predictions for specific analytes at lower concentrations without highly selective sensors.