{"id":13,"date":"2025-05-07T12:20:17","date_gmt":"2025-05-07T12:20:17","guid":{"rendered":"https:\/\/provalzeroclo.it\/?page_id=13"},"modified":"2025-05-08T08:26:39","modified_gmt":"2025-05-08T08:26:39","slug":"i-risultati","status":"publish","type":"page","link":"https:\/\/provalzeroclo.it\/en\/i-risultati\/","title":{"rendered":"The results"},"content":{"rendered":"
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Risultati e discussione<\/strong><\/h1>

The two main parameters that will be used to evaluate the goodness of the regression models VISSWIR spectra and chlorate\/perchlorate content are: Root Mean Square Error (RMSE) and \ud835\udc45\u0b36 \u200b\u200b(Martens et al., 1989; Naes, 2002). The RMSE parameter provides the possibility to evaluate the differences between the estimated response \ud835\udc80\u0311 and \u200b\u200bthe observed response \ud835\udc80. While, R2 is a parameter used to evaluate the model fitting. These parameters will be referred to the calibration, cross-validation and prediction phases. Meanwhile, to evaluate the importance of the contribution of the descriptive variables to the final model, both the variables significant to the description of Y must be taken into consideration (and the variables relevant to the description of X, summarized by parameters such as: regression vector, selectivity ratio and VIP scores. The classification performances will instead be evaluated on the basis of the parameters: Sensitivity, Specificity, Precision, Misclassification Error and Accuracy, referred to the calibration, cross-validation and prediction phase of the test set.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Risultati e discussione I due parametri principali che verranno utilizzati per valutare la bont\u00e0 dei modelli di regressione spettri VISSWIR e tenore di clorati\/perclorati sono: Root Mean Square Error (RMSE) e \ud835\udc45\u0b36 (Martens et al., 1989; Naes, 2002). Il parametro RMSE fornisce la possibilit\u00e0 di valutare le differenze tra la risposta stimata \ud835\udc80\u0311 e la risposta osservata \ud835\udc80. Mentre, R2 \u00e8 un parametro usato per valutare il fitting del modello. Tali parametri verranno riferiti alle fasi di calibrazione, convalida incrociata e predizione. Mentre, per valutare l’importanza del contributo delle variabili descrittive al modello finale si devono prendere in considerazione entrambe le variabili significative alla descrizione di Y (e le variabili rilevanti per la descrizione di X, sintetizzate da parametri quali: regression vector, selectivity ratio e VIP scores. Le performance di classificazione verranno, invece, valutate sulla base dei parametri: Sensitivity, Specificity, Precision, Misclassification Error ed Accuracy, riferiti alla fase di calibrazione, convalida incrociata e predizione del test set.<\/p>","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-13","page","type-page","status-publish","hentry","entry"],"_links":{"self":[{"href":"https:\/\/provalzeroclo.it\/en\/wp-json\/wp\/v2\/pages\/13","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/provalzeroclo.it\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/provalzeroclo.it\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/provalzeroclo.it\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/provalzeroclo.it\/en\/wp-json\/wp\/v2\/comments?post=13"}],"version-history":[{"count":10,"href":"https:\/\/provalzeroclo.it\/en\/wp-json\/wp\/v2\/pages\/13\/revisions"}],"predecessor-version":[{"id":57,"href":"https:\/\/provalzeroclo.it\/en\/wp-json\/wp\/v2\/pages\/13\/revisions\/57"}],"wp:attachment":[{"href":"https:\/\/provalzeroclo.it\/en\/wp-json\/wp\/v2\/media?parent=13"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}