Abstract
We present an application of the machine learning methods for modelling the retention constants in the thin layer chromatography. First a feature selection algorithm is applied to reduce the feature space and then the regression models are built with a help of the random forest algorithm. The models obtained in this way have better correlation with the experimental data than the reference models built with linear regression. They are also robust—the cross-validation tests shows that the accuracy on unseen data is on average identical to the cross-validated accuracy obtained on the training set.
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Kursa, M.B., Komsta, Ł., Rudnicki, W.R. (2011). The Robust Models of Retention for Thin Layer Chromatography. In: Czachórski, T., Kozielski, S., Stańczyk, U. (eds) Man-Machine Interactions 2. Advances in Intelligent and Soft Computing, vol 103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23169-8_19
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DOI: https://doi.org/10.1007/978-3-642-23169-8_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23168-1
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