Chapter Prediction of wine sensorial quality: a classification problem
When dealing with a wine, it is of interest to be able to predict its quality based on chemical and/or sensory variables. There is no agreement on what wine quality means, or how it should be assessed and it is often viewed in intrinsic (physicochemical, sensory) or extrinsic (price, prestige, conte...
Spremljeno u:
| Glavni autori: | , |
|---|---|
| Format: | Online |
| Jezik: | engleski |
| Izdano: |
Firenze University Press
2022
|
| Teme: | |
| Online pristup: | ONIX_20220601_9788855184618_557 |
| Oznake: |
Bez oznaka, Budi prvi tko označuje ovaj zapis!
|
| _version_ | 1869528503381131264 |
|---|---|
| author | Carpita, Maurizio GOLIA, Silvia |
| author_browse | Carpita, Maurizio GOLIA, Silvia |
| author_facet | Carpita, Maurizio GOLIA, Silvia |
| author_sort | Carpita, Maurizio |
| collection | Directory of Open Access Books |
| description | When dealing with a wine, it is of interest to be able to predict its quality based on chemical and/or sensory variables. There is no agreement on what wine quality means, or how it should be assessed and it is often viewed in intrinsic (physicochemical, sensory) or extrinsic (price, prestige, context) terms (Jackson, 2017). In this paper, the wine quality was evaluated by experienced judges who scored the wine on the base of a 0-10 scale, with 0 meaning very bad and 10 excellent, so, the resulting variable was categorical. The models applied to predict this variable provide the prediction of the occurrence probabilities of each of its categories. Nevertheless, jointly with this probabilities’ record, the practitioners need the predicted value (category) of the variable, so the statistical problem to be covered refers to the way in which this probabilities’ record is transformed into a single value. In this paper we compare the predictive performances of the default method (Bayes Classifier - BC), which assigns a unit to the most likely category, and other two methods (Maximum Difference Classifier and Maximum Ratio Classifier). The BC is the optimal criterion if one is interested in the accuracy of the classification, but, given that it favors the prevalent category most, when there is not a category of interest, it cannot be the best choice. The data under study concern the quality of the red variant of the Portuguese "Vinho Verde" wine (Cortez et al., 2009), measured on a 0-10 scale. Nevertheless, only 6 scores were used, with 2 scores with a very few number of observations, so this is the right context for predictive performance comparisons. In the study, we investigated different merging of categories and we used 11 explanatory variables to estimate the probabilities’ record of the wine quality variable. |
| format | Online |
| id | doab-20.500.12854ir-83601 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Firenze University Press |
| publisherStr | Firenze University Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-836012022-06-02T04:34:30Z Chapter Prediction of wine sensorial quality: a classification problem Carpita, Maurizio GOLIA, Silvia wine quality categorical classifier Bayes classifier When dealing with a wine, it is of interest to be able to predict its quality based on chemical and/or sensory variables. There is no agreement on what wine quality means, or how it should be assessed and it is often viewed in intrinsic (physicochemical, sensory) or extrinsic (price, prestige, context) terms (Jackson, 2017). In this paper, the wine quality was evaluated by experienced judges who scored the wine on the base of a 0-10 scale, with 0 meaning very bad and 10 excellent, so, the resulting variable was categorical. The models applied to predict this variable provide the prediction of the occurrence probabilities of each of its categories. Nevertheless, jointly with this probabilities’ record, the practitioners need the predicted value (category) of the variable, so the statistical problem to be covered refers to the way in which this probabilities’ record is transformed into a single value. In this paper we compare the predictive performances of the default method (Bayes Classifier - BC), which assigns a unit to the most likely category, and other two methods (Maximum Difference Classifier and Maximum Ratio Classifier). The BC is the optimal criterion if one is interested in the accuracy of the classification, but, given that it favors the prevalent category most, when there is not a category of interest, it cannot be the best choice. The data under study concern the quality of the red variant of the Portuguese "Vinho Verde" wine (Cortez et al., 2009), measured on a 0-10 scale. Nevertheless, only 6 scores were used, with 2 scores with a very few number of observations, so this is the right context for predictive performance comparisons. In the study, we investigated different merging of categories and we used 11 explanatory variables to estimate the probabilities’ record of the wine quality variable. 2022-06-02T04:34:28Z 2022-06-02T04:34:28Z 2022-06-01T12:21:01Z 2021 chapter ONIX_20220601_9788855184618_557 2704-5846 https://library.oapen.org/handle/20.500.12657/56372 9788855184618 https://directory.doabooks.org/handle/20.500.12854/83601 eng Proceedings e report open access image/jpeg Attribution 4.0 International https://library.oapen.org/bitstream/20.500.12657/56372/1/26262.pdf Firenze University Press 10.36253/978-88-5518-461-8.44 10.36253/978-88-5518-461-8.44 2ec4474d-93b1-4cfa-b313-9c6019b51b1a 9788855184618 4 Florence open access |
| spellingShingle | wine quality categorical classifier Bayes classifier Carpita, Maurizio GOLIA, Silvia Chapter Prediction of wine sensorial quality: a classification problem |
| title | Chapter Prediction of wine sensorial quality: a classification problem |
| title_full | Chapter Prediction of wine sensorial quality: a classification problem |
| title_fullStr | Chapter Prediction of wine sensorial quality: a classification problem |
| title_full_unstemmed | Chapter Prediction of wine sensorial quality: a classification problem |
| title_short | Chapter Prediction of wine sensorial quality: a classification problem |
| title_sort | chapter prediction of wine sensorial quality a classification problem |
| topic | wine quality categorical classifier Bayes classifier |
| topic_facet | wine quality categorical classifier Bayes classifier |
| url | ONIX_20220601_9788855184618_557 |
| work_keys_str_mv | AT carpitamaurizio chapterpredictionofwinesensorialqualityaclassificationproblem AT goliasilvia chapterpredictionofwinesensorialqualityaclassificationproblem |