如何在R中与kNN进行交叉验证时构造混淆矩阵

问题描述 投票：1回答：1

我试着在另一个论坛上问这个问题，但是在没有得到答案之后，我在这里重新制定它并且对我的问题更具体一些。我的数据集看起来像这样：

> head(knnresults)
   ACTIVITY_X ACTIVITY_Y ACTIVITY_Z classification
1:         40         47         62        Feeding
2:         60         74         95       Standing
3:         62         63         88       Standing
4:         60         56         82       Standing
5:         66         61         90       Standing
6:         60         53         80       Standing

列classification有三个不同的类别Feeding，Standing和Foraging。

我现在正在选择一个最佳的k值，这就是为什么我使用其他80％的数据将20％的数据分类为训练。分类基于前三列中的值。将选择显示最高准确度的k值用于将来的分类分析。

这是我一直在使用的脚本：

library(ISLR)
library(caret)
library(lattice)
library(ggplot2)

# Split the data for cross validation:
indxTrain <- createDataPartition(y = knnresults$classification,p = 0.8,list = FALSE)
training <- knnresults[indxTrain,]
testing <- knnresults[-indxTrain,]

# Run k-NN:
set.seed(400)
ctrl <- trainControl(method="repeatedcv",repeats = 3)
knnFit <- train(classification ~ ., data = training, method = "knn", trControl = ctrl, preProcess = c("center","scale"),tuneLength = 20)
knnFit

#Plotting different k values against accuracy (based on repeated cross validation)
plot(knnFit)

首先，让我道歉，因为我是R的新手，我不确定这个剧本的合法性。如果发现错误，我将非常乐意接受任何纠正建议。

其次，如何根据此代码访问分类的混淆矩阵？这对于计算与分类相关联的性能指标很重要。

我可以dput()下面的数据集，如果这可以帮助：

> dput(knnresults)
structure(list(ACTIVITY_X = c(40L, 60L, 62L, 60L, 66L, 60L, 57L, 
54L, 52L, 93L, 80L, 14L, 61L, 51L, 40L, 20L, 21L, 5L, 53L, 48L, 
73L, 73L, 21L, 29L, 63L, 59L, 57L, 51L, 53L, 67L, 72L, 74L, 70L, 
60L, 74L, 85L, 77L, 68L, 58L, 80L, 34L, 45L, 34L, 60L, 75L, 62L, 
66L, 51L, 53L, 48L, 62L, 62L, 57L, 5L, 1L, 12L, 23L, 5L, 4L, 
0L, 13L, 45L, 44L, 31L, 68L, 88L, 43L, 70L, 18L, 83L, 71L, 67L, 
75L, 74L, 49L, 90L, 44L, 64L, 57L, 22L, 29L, 52L, 37L, 32L, 120L, 
45L, 22L, 54L, 30L, 9L, 27L, 14L, 3L, 29L, 12L, 61L, 60L, 29L, 
15L, 7L, 6L, 0L, 2L, 0L, 4L, 1L, 7L, 0L, 0L, 0L, 0L, 0L, 1L, 
23L, 49L, 46L, 8L, 31L, 45L, 60L, 37L, 61L, 52L, 51L, 38L, 86L, 
60L, 41L, 43L, 40L, 42L, 42L, 48L, 64L, 71L, 59L, 0L, 27L, 12L, 
3L, 0L, 0L, 8L, 21L, 6L, 2L, 7L, 4L, 3L, 3L, 46L, 46L, 59L, 53L, 
37L, 44L, 39L, 49L, 37L, 47L, 17L, 36L, 32L, 33L, 26L, 12L, 8L, 
31L, 35L, 27L, 27L, 24L, 17L, 35L, 39L, 28L, 54L, 5L, 0L, 0L, 
0L, 0L, 17L, 22L, 25L, 12L, 0L, 5L, 41L, 51L, 66L, 39L, 32L, 
53L, 43L, 40L, 44L, 45L, 48L, 51L, 41L, 45L, 39L, 46L, 59L, 31L, 
5L, 24L, 18L, 5L, 15L, 13L, 0L, 26L, 0L), ACTIVITY_Y = c(47L, 
74L, 63L, 56L, 61L, 53L, 40L, 41L, 49L, 32L, 54L, 13L, 99L, 130L, 
38L, 14L, 6L, 5L, 94L, 96L, 38L, 43L, 29L, 47L, 66L, 47L, 38L, 
31L, 36L, 35L, 38L, 72L, 54L, 44L, 45L, 51L, 80L, 48L, 39L, 85L, 
42L, 39L, 37L, 75L, 36L, 45L, 32L, 35L, 41L, 26L, 99L, 163L, 
124L, 0L, 0L, 24L, 37L, 0L, 6L, 0L, 29L, 29L, 26L, 27L, 54L, 
147L, 82L, 98L, 12L, 83L, 97L, 104L, 128L, 81L, 42L, 102L, 60L, 
79L, 58L, 15L, 14L, 75L, 75L, 40L, 130L, 40L, 13L, 54L, 42L, 
7L, 10L, 3L, 0L, 15L, 8L, 75L, 55L, 26L, 18L, 1L, 13L, 0L, 0L, 
0L, 1L, 0L, 4L, 0L, 0L, 0L, 0L, 0L, 0L, 17L, 45L, 38L, 10L, 31L, 
52L, 36L, 65L, 97L, 45L, 59L, 49L, 92L, 51L, 34L, 21L, 20L, 29L, 
28L, 22L, 32L, 30L, 86L, 0L, 15L, 7L, 4L, 0L, 0L, 0L, 11L, 3L, 
0L, 1L, 3L, 1L, 0L, 72L, 62L, 98L, 55L, 26L, 39L, 28L, 81L, 20L, 
52L, 12L, 48L, 24L, 40L, 30L, 5L, 6L, 40L, 37L, 33L, 26L, 17L, 
14L, 39L, 27L, 28L, 67L, 0L, 0L, 0L, 0L, 0L, 10L, 12L, 14L, 7L, 
0L, 2L, 39L, 67L, 74L, 28L, 23L, 57L, 34L, 36L, 36L, 37L, 46L, 
43L, 73L, 65L, 31L, 64L, 128L, 17L, 3L, 12L, 17L, 0L, 9L, 7L, 
0L, 17L, 0L), ACTIVITY_Z = c(62L, 95L, 88L, 82L, 90L, 80L, 70L, 
68L, 71L, 98L, 97L, 19L, 116L, 140L, 55L, 24L, 22L, 7L, 108L, 
107L, 82L, 85L, 36L, 55L, 91L, 75L, 69L, 60L, 64L, 76L, 81L, 
103L, 88L, 74L, 87L, 99L, 111L, 83L, 70L, 117L, 54L, 60L, 50L, 
96L, 83L, 77L, 73L, 62L, 67L, 55L, 117L, 174L, 136L, 5L, 1L, 
27L, 44L, 5L, 7L, 0L, 32L, 54L, 51L, 41L, 87L, 171L, 93L, 120L, 
22L, 117L, 120L, 124L, 148L, 110L, 65L, 136L, 74L, 102L, 81L, 
27L, 32L, 91L, 84L, 51L, 177L, 60L, 26L, 76L, 52L, 11L, 29L, 
14L, 3L, 33L, 14L, 97L, 81L, 39L, 23L, 7L, 14L, 0L, 2L, 0L, 4L, 
1L, 8L, 0L, 0L, 0L, 0L, 0L, 1L, 29L, 67L, 60L, 13L, 44L, 69L, 
70L, 75L, 115L, 69L, 78L, 62L, 126L, 79L, 53L, 48L, 45L, 51L, 
50L, 53L, 72L, 77L, 104L, 0L, 31L, 14L, 5L, 0L, 0L, 8L, 24L, 
7L, 2L, 7L, 5L, 3L, 3L, 85L, 77L, 114L, 76L, 45L, 59L, 48L, 95L, 
42L, 70L, 21L, 60L, 40L, 52L, 40L, 13L, 10L, 51L, 51L, 43L, 37L, 
29L, 22L, 52L, 47L, 40L, 86L, 5L, 0L, 0L, 0L, 0L, 20L, 25L, 29L, 
14L, 0L, 5L, 57L, 84L, 99L, 48L, 39L, 78L, 55L, 54L, 57L, 58L, 
66L, 67L, 84L, 79L, 50L, 79L, 141L, 35L, 6L, 27L, 25L, 5L, 17L, 
15L, 0L, 31L, 0L), classification = c("Feeding", "Standing", 
"Standing", "Standing", "Standing", "Standing", "Feeding", "Feeding", 
"Feeding", "Standing", "Standing", "Foraging", "Standing", "Standing", 
"Feeding", "Foraging", "Foraging", "Foraging", "Standing", "Standing", 
"Standing", "Standing", "Feeding", "Feeding", "Standing", "Feeding", 
"Feeding", "Feeding", "Feeding", "Feeding", "Standing", "Standing", 
"Standing", "Feeding", "Standing", "Standing", "Standing", "Standing", 
"Feeding", "Standing", "Feeding", "Feeding", "Feeding", "Standing", 
"Standing", "Feeding", "Feeding", "Feeding", "Feeding", "Feeding", 
"Standing", "Standing", "Standing", "Foraging", "Foraging", "Foraging", 
"Feeding", "Foraging", "Foraging", "Foraging", "Foraging", "Feeding", 
"Feeding", "Feeding", "Standing", "Standing", "Standing", "Standing", 
"Foraging", "Standing", "Standing", "Standing", "Standing", "Standing", 
"Feeding", "Standing", "Feeding", "Standing", "Standing", "Foraging", 
"Foraging", "Standing", "Feeding", "Feeding", "Standing", "Feeding", 
"Foraging", "Feeding", "Feeding", "Foraging", "Foraging", "Foraging", 
"Foraging", "Foraging", "Foraging", "Standing", "Standing", "Feeding", 
"Foraging", "Foraging", "Foraging", "Foraging", "Foraging", "Foraging", 
"Foraging", "Foraging", "Foraging", "Foraging", "Foraging", "Foraging", 
"Foraging", "Foraging", "Foraging", "Foraging", "Feeding", "Feeding", 
"Foraging", "Feeding", "Feeding", "Feeding", "Feeding", "Standing", 
"Feeding", "Feeding", "Feeding", "Standing", "Standing", "Feeding", 
"Feeding", "Feeding", "Feeding", "Feeding", "Feeding", "Feeding", 
"Standing", "Standing", "Foraging", "Foraging", "Foraging", "Foraging", 
"Foraging", "Foraging", "Foraging", "Foraging", "Foraging", "Foraging", 
"Foraging", "Foraging", "Foraging", "Foraging", "Standing", "Feeding", 
"Standing", "Feeding", "Feeding", "Feeding", "Feeding", "Standing", 
"Feeding", "Feeding", "Foraging", "Feeding", "Feeding", "Feeding", 
"Feeding", "Foraging", "Foraging", "Feeding", "Feeding", "Feeding", 
"Feeding", "Foraging", "Foraging", "Feeding", "Feeding", "Feeding", 
"Standing", "Foraging", "Foraging", "Foraging", "Foraging", "Foraging", 
"Foraging", "Foraging", "Foraging", "Foraging", "Foraging", "Foraging", 
"Feeding", "Standing", "Standing", "Feeding", "Feeding", "Feeding", 
"Feeding", "Feeding", "Feeding", "Feeding", "Feeding", "Feeding", 
"Feeding", "Feeding", "Feeding", "Feeding", "Standing", "Feeding", 
"Foraging", "Foraging", "Foraging", "Foraging", "Foraging", "Foraging", 
"Foraging", "Foraging", "Foraging")), row.names = c(NA, -215L
), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x0000000002531ef0>)

任何输入真的很感激！

r r-caret nearest-neighbor rpart
1个回答
1
投票

这是一个可重复的例子：

library(caret)
train_set<-createDataPartition(iris$Species,p=0.8,list=FALSE)
valid_set<-iris[-train_set,]
train_set<-iris[train_set,]
ctrl<-trainControl(method="cv",number=5)
set.seed(233)
mk<-train(Species~.,data=train_set,method="knn",trControl = ctrl,metric="Accuracy")

获得混淆矩阵。理想情况下，最好将您的训练与测试或验证集的predicted值进行比较。

编辑：要检索表格，只需执行以下操作：

confusionMatrix(mk)["table"]
$table
            Reference
Prediction       setosa versicolor  virginica
  setosa     33.3333333  0.0000000  0.0000000
  versicolor  0.0000000 32.5000000  2.5000000
  virginica   0.0000000  0.8333333 30.8333333

原版的

 confusionMatrix(mk)

结果：

Cross-Validated (5 fold) Confusion Matrix 

(entries are percentual average cell counts across resamples)

            Reference
Prediction   setosa versicolor virginica
  setosa       33.3        0.0       0.0
  versicolor    0.0       31.7       1.7
  virginica     0.0        1.7      31.7

 Accuracy (average) : 0.9667
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