Question

我正在使用虹膜数据集在R中练习SVM，我想从模型中获取特征权重/系数，但是鉴于我的输出给了我32个支持向量，我想我可能会误解某些东西。假设我要分析四个变量，我将得到四个。我知道在使用svm()函数时有一种方法，但是我试图使用插入符号中的train()函数来生成我的SVM。

library(caret)

# Define fitControl
fitControl <- trainControl(## 5-fold CV
              method = "cv",
              number = 5,
              classProbs = TRUE,
              summaryFunction = twoClassSummary )

# Define Tune
grid<-expand.grid(C=c(2^-5,2^-3,2^-1))

########## 
df<-iris head(df)
df<-df[df$Species!='setosa',]
df$Species<-as.character(df$Species)
df$Species<-as.factor(df$Species)

# set random seed and run the model
set.seed(321)
svmFit1 <- train(x = df[-5],
                 y=df$Species,
                 method = "svmLinear", 
                 trControl = fitControl,
                 preProc = c("center","scale"),
                 metric="ROC",
                 tuneGrid=grid )
svmFit1

我以为只是svmFit1$finalModel@coef，但是当我认为应该得到4时我得到32个向量。为什么？

Answer 1

所以coef不是支持向量的权重W。这是docs中ksvm类的相关部分：

coef相应的系数乘以训练标签。

要获得所需的内容，您需要执行以下操作：

coefs <- svmFit1$finalModel@coef[[1]]
mat <- svmFit1$finalModel@xmatrix[[1]]

coefs %*% mat

有关可重现的示例，请参见下文。

library(caret)
#> Loading required package: lattice
#> Loading required package: ggplot2
#> Warning: package 'ggplot2' was built under R version 3.5.2

# Define fitControl
fitControl <- trainControl(
  method = "cv",
  number = 5,
  classProbs = TRUE,
  summaryFunction = twoClassSummary
)

# Define Tune
grid <- expand.grid(C = c(2^-5, 2^-3, 2^-1))

########## 
df <- iris 

df<-df[df$Species != 'setosa', ]
df$Species <- as.character(df$Species)
df$Species <- as.factor(df$Species)

# set random seed and run the model
set.seed(321)
svmFit1 <- train(x = df[-5],
                 y=df$Species,
                 method = "svmLinear", 
                 trControl = fitControl,
                 preProc = c("center","scale"),
                 metric="ROC",
                 tuneGrid=grid )

coefs <- svmFit1$finalModel@coef[[1]]
mat <- svmFit1$finalModel@xmatrix[[1]]

coefs %*% mat
#>      Sepal.Length Sepal.Width Petal.Length Petal.Width
#> [1,]   -0.1338791  -0.2726322    0.9497457    1.027411

^{由reprex package（v0.2.1.9000）于2019-06-11创建}

来源

Answer 2

随着越来越多的人开始从Caret转向Tidymodels，我想我会在2020年8月为Tidymodels投放上述解决方案的一个版本，因为到目前为止我还没有看到太多关于此问题的讨论，而且做起来也不那么容易。

此处概述了主要步骤，但请查看结尾处的链接以详细了解采用这种方法的原因。

1。获取最终模型

set.seed(2020)

# Assuming kernlab linear SVM

# Grid Search Parameters
tune_rs <- tune_grid(
  model_wf,
  train_folds,
  grid = param_grid,
  metrics = classification_measure,
  control = control_grid(save_pred = TRUE)
)

# Finalise workflow with the parameters for best accuracy
best_accuracy <- select_best(tune_rs, "accuracy")

svm_wf_final <- finalize_workflow(
  model_wf,
  best_accuracy
)

# Fit on your final model on all available data at the end of experiment
final_model <- fit(svm_wf_final, data)
# fit takes a model spec and executes the model fit routine (Parsnip)
  # model_spec, formula and data to fit upon

2。提取KSVM对象，提取所需信息，计算变量重要性

ksvm_obj <- pull_workflow_fit(final_model)$fit
# Pull_workflow_fit returns the parsnip model fit object
# $fit returns the object produced by the fitting fn (which is what we need! and is dependent on the engine)

coefs <- ksvm_obj@coef[[1]]
# first bit of info we need are the coefficients from the linear fit

mat <- ksvm_obj@xmatrix[[1]]
# xmatrix that we need to matrix multiply against

var_impt <- coefs %*% mat
# var importance

参考：

使用Caret提取支持向量的权重：Linear SVM and extracting the weights
变量重要性（本文的最后部分）：http://www.rebeccabarter.com/blog/2020-03-25_machine_learning/#finalize-the-workflow

线性SVM并提取权重

2 个答案: