data_classifying: Predicting product classes via the machine learning model

In PriceIndices: Calculating Bilateral and Multilateral Price Indexes

Predicting product classes via the machine learning model

Description

This function predicts product class levels via the selected machine learning model.

Usage

data_classifying(model = list(), data)

Arguments

model	A list of 8 elements which identify the previously built machine learning model (the list is obtained via the model_classification function).
data	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.

Value

This function provides the indicated data set with an additional column, i.e. class_predicted, which is obtained by using the selected machine learning model.

Examples

#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,class="coicop6",grid=my.grid,
indicators=c("description","codeIN", "grammage"),key_words=c("uht"),rounds=60)
#Data classification
data_classifying(ML, data_test)

PriceIndices documentation built on Oct. 10, 2024, 1:07 a.m.