支持向量机在R语言中的应用 - CSDN文库

支持向量机

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“ ”有问题到淘宝找大数据部落就可以了。

> ## SVM in R

> ## classify cats into male / female by heart and body weight

> library(e1071)

> data(cats, package="MASS")

> inputData <- data.frame(cats[, c (2,3)],

+ response = as.factor(cats$Sex))

> ## linear kernel

> svmfit <- svm(response ~ ., data = inputData, kernel = "linear",

+ cost = 10, scale = FALSE)

> plot(svmfit, inputData)

> compareTable <- table (inputData$response, predict(svmfit))

> mean(inputData$response != predict(svmfit))

[1] 0.1944444

> compareTable

F M

F 33 14

M 14 83

> ## radial kernel

> svmfit <- svm(response ~ ., data = inputData, kernel = "radial",

+ cost = 10, scale = FALSE)

> plot(svmfit, inputData)

> compareTable <- table (inputData$response, predict(svmfit))

> mean(inputData$response != predict(svmfit))

[1] 0.1875

> ## radial kernel

> svmfit <- svm(response ~ ., data = inputData, kernel = "radial",

+ cost = 10, scale = FALSE)

> plot(svmfit, inputData)

> compareTable <- table (inputData$response, predict(svmfit))

> mean(inputData$response != predict(svmfit))

[1] 0.1875

>

> ## Prepare training and test data

> set.seed(100)

> rowIndices <- 1 : nrow(inputData)

> sampleSize <- 0.8 * length(rowIndices)

> trainingRows <- sample (rowIndices, sampleSize)

> trainingData <- inputData[trainingRows, ]

> testData <- inputData[-trainingRows, ]

> ## tune to select best gamma and cost

> tuned <- tune.svm(response ~., data = trainingData,

+ gamma = 10^(-6:1), cost = 10^(-1:2))

> summary(tuned)

Parameter tuning of 'svm':

- sampling method: 10-fold cross validation

- best parameters:

gamma cost

0.01 100

- best performance: 0.2439394

- Detailed performance results:

gamma cost error dispersion

1 0.000001 0.1 0.3325758 0.16269565

2 0.000010 0.1 0.3325758 0.16269565

3 0.000100 0.1 0.3325758 0.16269565

4 0.001000 0.1 0.3325758 0.16269565

5 0.010000 0.1 0.3325758 0.16269565

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