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SVM en R, el paquete e1071

SVM en R, el paquete e1071. > names(tinto) [1] "fixed.acidity" "volatile.acidity" [3] "citric.acid" "residual.sugar" [5] "chlorides" "free.sulfur.dioxide" [7] "total.sulfur.dioxide" "density " [9] "pH" "sulphates"

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SVM en R, el paquete e1071

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  1. SVM en R, el paquete e1071 > names(tinto) [1] "fixed.acidity" "volatile.acidity" [3] "citric.acid" "residual.sugar" [5] "chlorides" "free.sulfur.dioxide" [7] "total.sulfur.dioxide" "density" [9] "pH" "sulphates" [11] "alcohol" "quality" Trabajamos con el dataset de vinos tintos verdes portugueses > unique(tinto$quality) [1] 5 6 7 4 8 3 > qualcod <- ifelse(tinto$quality %in% c(7,8), 1, -1) > table(qualcod) qualcod -1 1 1382 217 Separamos en dos clases los vinos de categoría 7 y 8 del resto

  2. SVM en R, el paquete e1071 • > ind <- 1:nrow(tinto) • > testind <-sample(ind, trunc(length(ind)/3)) • > tinto <- data.frame(tinto, qualcod) • > testset <- tinto[testind, -12] • > trainset <- tinto[-testind, -12] • > tinto.tunesvm <- tune.svm(as.factor(qualcod)~., • data=trainset, gamma=2^(-3:3), cost=2^(0:4)) • > summary(tinto.tunesvm) • Parameter tuning of ‘svm’: • - sampling method: 10-fold cross validation • - best parameters: gamma cost 1 2 • - best performance: 0.0956974 • Detailed performance results: • gamma cost error dispersion • 1 0.125 1 0.11728090 0.01498582 • 2 0.250 1 0.11447716 0.02505541 • ...

  3. SVM en R, el paquete e1071 Con la función tune.svm se puede recorrer una grilla de valores de los parámetros y obtener la mejor combinación. En el caso anterior: gamma =1 C=2 > plot(tinto.tunesvm) > tinto.svm <- tinto.tunesvm$best.model > tinto.svm.pred <- predict(tinto.svm, testset[,-12]) > table(predicho=tinto.svm.pred, verdadero=testset[,12]) verdadero predicho -1 1 -1 451 50 1 8 24 > (8+50)/(nrow(testset)) [1] 0.1088180

  4. Clasificando por knn: > tinto.knn <- knn(trainset[, -12], testset[, -12], trainset$qualcod, k=15) > table(predicho=tinto.knn, verdadero=testset[,12]) > (70+2)/nrow(testset) verdadero predicho -1 1 -1 457 70 1 2 4 > (70+2)/(nrow(testset)) [1] 0.1350844

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