We attempt here to build a classifier, using methods of machine learning (ML), which based on 12 physical and chemical measurements of a wine predict if is good or not as good (poor) determined by experts. The dataset to our disposal consists of 5000 observation. The explanatory variables include measurements of, e.g. alcohol, chloride, citric acid concentrations, or of the wine color (white, red). There is also a binary variable quality, determined by an expert assessment, which we will be trying to predict for new observations using the ML model.

On Computing Dynamic SWATH Windows

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