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This function predicts product COICOP levels via the selected machine learning model.
data_classifying(model = list(), data)This function provides the indicated data set with an additional column, i.e. coicop_predicted, which is obtained by using the selected machine learning model.
A list of 8 elements which identify the previously built machine learning model (the list is obtained via the model_classification function).
A data set for the model (products with their characteristics). This data set must contain all the columns which were used in the built model.
#Building the model
my.grid=list(eta=c(0.01,0.02,0.05),subsample=c(0.5,0.8))
data_train<-dplyr::filter(dataCOICOP,dataCOICOP$time<=as.Date("2021-10-01"))
data_test<-dplyr::filter(dataCOICOP,dataCOICOP$time==as.Date("2021-11-01"))
ML<-model_classification(data_train,data_test,coicop="coicop6",grid=my.grid,
indicators=c("description","codeIN"),key_words=c("uht"),rounds=60)
#Data classification
data_classifying(ML, data_test)
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