如何使用svm为R中的多个类别创建分类模型？
SVM是一种监督型机器学习算法，可用于分类或回归挑战，但大多数情况下我们将其用于分类。使用svm的分类也可以针对两个或更多类别进行。在R中，我们可以简单地使用e1071包的svm函数。
示例
考虑虹膜数据-
str(iris)
输出结果
'data.frame': 150 obs. of 5 variables: $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ... $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ... $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ... $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ... $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
示例
head(iris,20)
输出结果
Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1 5.1 3.5 1.4 0.2 setosa 2 4.9 3.0 1.4 0.2 setosa 3 4.7 3.2 1.3 0.2 setosa 4 4.6 3.1 1.5 0.2 setosa 5 5.0 3.6 1.4 0.2 setosa 6 5.4 3.9 1.7 0.4 setosa 7 4.6 3.4 1.4 0.3 setosa 8 5.0 3.4 1.5 0.2 setosa 9 4.4 2.9 1.4 0.2 setosa 10 4.9 3.1 1.5 0.1 setosa 11 5.4 3.7 1.5 0.2 setosa 12 4.8 3.4 1.6 0.2 setosa 13 4.8 3.0 1.4 0.1 setosa 14 4.3 3.0 1.1 0.1 setosa 15 5.8 4.0 1.2 0.2 setosa 16 5.7 4.4 1.5 0.4 setosa 17 5.4 3.9 1.3 0.4 setosa 18 5.1 3.5 1.4 0.3 setosa 19 5.7 3.8 1.7 0.3 setosa 20 5.1 3.8 1.5 0.3 setosa
加载e1071软件包并创建svm模型来预测物种-
示例
library(e1071) model_1<-svm(iris$Species~.,iris) model_1
输出结果
Call:
svm(formula = iris$Species ~ ., data = iris)
Parameters:
SVM-Type: C-classification
SVM-Kernel: radial
cost: 1
Number of Support Vectors: 51示例
Consider the below data frame: x1<-rnorm(20,1,1.05) x2<-rnorm(20,1,1.05) x3<-rnorm(20,1,1.05) y1<-factor(sample(LETTERS[1:4],20,replace=TRUE)) df1<-data.frame(x1,x2,x3,y1) df1
输出结果
x1 x2 x3 y1 1 -0.16972931 0.7246676 1.45289129 D 2 0.70684500 2.2078975 1.64698238 D 3 0.75542931 1.7193236 1.31461683 A 4 -0.01975337 0.6848992 0.80361117 D 5 0.86139532 1.3101784 0.35196665 C 6 -0.53543129 -0.1596975 1.06723416 B 7 -0.81283371 2.1653334 1.93182228 A 8 -0.31556364 -0.4410462 1.61967614 A 9 1.52678513 1.9356670 0.04359926 D 10 1.24594463 0.6215577 0.71009713 A 11 1.53888275 0.7491438 2.08191985 D 12 1.19568488 0.6597553 2.40080721 C 13 -0.18610407 0.3972270 2.23357076 D 14 0.56453388 0.5964609 0.94534907 D 15 1.98699347 0.8026872 -0.68205488 D 16 2.00788377 0.9093129 3.24888927 B 17 1.69652350 0.5379913 0.67402105 A 18 1.28221388 1.7807587 2.06529243 B 19 0.17814671 -0.4299207 0.47859582 D 20 2.82514461 1.9284933 1.59796618 D
创建svm模型以预测y1-
示例
model_2<-svm(df1$y1~.,df1) model_2
输出结果
Call:
svm(formula = df1$y1 ~ ., data = df1)
Parameters:
SVM-Type: C-classification
SVM-Kernel: radial
cost: 1
Number of Support Vectors: 20