在R中插入符号中调整模型时如何保持参数恒定？

Question

如下代码：

require(caret)
require(plyr)

portuguese_scores = read.table("https://raw.githubusercontent.com/JimGorman17/Datasets/master/student-por.csv",sep=";",header=TRUE, stringsAsFactors = FALSE)
portuguese_scores <- portuguese_scores[,!names(portuguese_scores) %in% c("school", "age", "G1", "G2")]
median_score <- summary(portuguese_scores$G3)['Median']
portuguese_scores$score_gte_than_median <- as.factor(median_score<=portuguese_scores$G3)
portuguese_scores <- portuguese_scores[,!names(portuguese_scores) %in% c("G3")]

portuguese_scores$sex <- as.numeric(mapvalues(portuguese_scores$sex, from = c("M", "F"), to = c(0, 1)))
portuguese_scores$address <- as.numeric(mapvalues(portuguese_scores$address, from = c("U", "R"), to = c(0, 1)))
portuguese_scores$famsize <- as.numeric(mapvalues(portuguese_scores$famsize, from = c("LE3", "GT3"), to = c(0, 1)))
portuguese_scores$Pstatus <- as.numeric(mapvalues(portuguese_scores$Pstatus, from = c("T", "A"), to = c(0, 1)))
portuguese_scores$Mjob <- as.numeric(mapvalues(portuguese_scores$Mjob, from = c("at_home","health","other","services","teacher"), to = c(0, 1,2,3,4)))
portuguese_scores$Fjob <- as.numeric(mapvalues(portuguese_scores$Fjob, from = c("at_home","health","other","services","teacher"), to = c(0, 1,2,3,4)))
portuguese_scores$reason <- as.numeric(mapvalues(portuguese_scores$reason, from = c("course","home","other","reputation"), to = c(0, 1,2,3)))
portuguese_scores$guardian <- as.numeric(mapvalues(portuguese_scores$guardian, from = c("father","mother","other"), to = c(0, 1,2)))
portuguese_scores$schoolsup <- as.numeric(mapvalues(portuguese_scores$schoolsup, from = c("no","yes"), to = c(0, 1)))
portuguese_scores$famsup <- as.numeric(mapvalues(portuguese_scores$famsup, from = c("no","yes"), to = c(0, 1)))
portuguese_scores$paid <- as.numeric(mapvalues(portuguese_scores$paid, from = c("no","yes"), to = c(0, 1)))
portuguese_scores$activities <- as.numeric(mapvalues(portuguese_scores$activities, from = c("no","yes"), to = c(0, 1)))
portuguese_scores$nursery <- as.numeric(mapvalues(portuguese_scores$nursery, from = c("no","yes"), to = c(0, 1)))
portuguese_scores$higher <- as.numeric(mapvalues(portuguese_scores$higher, from = c("no","yes"), to = c(0, 1)))
portuguese_scores$internet <- as.numeric(mapvalues(portuguese_scores$internet, from = c("no","yes"), to = c(0, 1)))
portuguese_scores$romantic <- as.numeric(mapvalues(portuguese_scores$romantic, from = c("no","yes"), to = c(0, 1)))

normalize <- function(x){ return( (x - min(x) )/( max(x) - min(x) ) )}
port_n <- data.frame(lapply(portuguese_scores[1:28], normalize), portuguese_scores[29])

set.seed(123)

train_sample <- sample(nrow(port_n), .9 * nrow(port_n))
port_train <- port_n[train_sample,]
port_test <- port_n[-train_sample,]

out1 <- train(port_train[,1:28], port_train[,29], method = "svmRadial")
out1

生成以下输出：

Support Vector Machines with Radial Basis Function Kernel 

584 samples
 28 predictor
  2 classes: 'FALSE', 'TRUE' 

No pre-processing
Resampling: Bootstrapped (25 reps) 
Summary of sample sizes: 584, 584, 584, 584, 584, 584, ... 
Resampling results across tuning parameters:

  C     Accuracy   Kappa      Accuracy SD  Kappa SD  
  0.25  0.7383930  0.4633478  0.02782725   0.05484469
  0.50  0.7382364  0.4637857  0.02883617   0.05763094
  1.00  0.7290191  0.4456935  0.02570423   0.05180727

Tuning parameter 'sigma' was held constant at a value of 0.02166535
Accuracy was used to select the optimal model using  the largest value.
The final values used for the model were sigma = 0.02166535 and C = 0.25. 

我的问题：

• 如何保持c不变（为0.25）并找到最佳sigma？

更新（对所有亲密的选民）：

• 您看到插入符号如何使sigma保持恒定并针对C优化吗？
• 我只是想做相反的事情。 我想保持C不变并为sigma优化。
• 而且我相信有调整参数可以帮助我做到这一点，我只是不知道语法。

Answer 1

为此，您需要使用tuneGrid参数。 您需要为参数创建自己的对，然后对其进行测试。

例如，由于要在所有情况下测试C = 0.25，因此需要创建一个如下所示的data.frame：

svmGrid <- data.frame(C=rep(0.25,10), sigma=1:10/100)

C具有相同的值（0.25），而σ具有不同的值以进行优化。 您需要自己为sigma提供这些值（这只是一个示例-使用任意数量）。

换句话说，根据上面的data.frame，您的svm模型将被测试10次。 每次C都是常数且等于0.25时，sigma的取值范围为0.01到0.1，步长为0.01。 将进行10个测试，并选择最佳组合。

然后像这样运行模型：

#adding the tuneGrid argument
out1 <- train(port_train[,1:28], port_train[,29], method = "svmRadial", tuneGrid=svmGrid)

输出：

> out1
Support Vector Machines with Radial Basis Function Kernel 

584 samples
 28 predictor
  2 classes: 'FALSE', 'TRUE' 

No pre-processing
Resampling: Bootstrapped (25 reps) 

Summary of sample sizes: 584, 584, 584, 584, 584, 584, ... 

Resampling results across tuning parameters:

  sigma  Accuracy   Kappa      Accuracy SD  Kappa SD  
  0.01   0.7297315  0.4417768  0.03082764   0.06044173
  0.02   0.7312643  0.4474754  0.03289345   0.06567919
  0.03   0.7301472  0.4468033  0.03618417   0.07187019
  0.04   0.7288286  0.4463212  0.03609275   0.07200966
  0.05   0.7281374  0.4466735  0.03569426   0.07055105
  0.06   0.7238098  0.4400315  0.03348371   0.06666725
  0.07   0.7213752  0.4364012  0.03467845   0.06849882
  0.08   0.7175949  0.4286502  0.04013475   0.08014780
  0.09   0.7042396  0.3981745  0.04346037   0.08864786
  0.10   0.6651296  0.3061489  0.06450228   0.14079631

Tuning parameter 'C' was held constant at a value of 0.25
Accuracy was used to select the optimal model using  the largest value.
The final values used for the model were sigma = 0.02 and C = 0.25. 

并且您有优化的sigma！