Question

当我们为因子字段提供标签时会发生什么变化？在下面的代码中，我曾将标签分配为0和1，下次我将标签分配为0和10 ^ 6。根据我的知识，标签只是提供类别的替代名称，在这种情况下是男性和女性。请注意，我提供了数字标签而不是字符标签。

似乎标签正在为改变数据点的eucladian距离的类别提供某种数字权重。下面提供了两个相应结果的问题代码

数据集

> head(dataset)
   User.ID Gender Age EstimatedSalary Purchased
1 15624510      1  19           19000         0
2 15810944      1  35           20000         0
3 15668575      0  26           43000         0
4 15603246      0  27           57000         0
5 15804002      1  19           76000         0
6 15728773      1  27           58000         0

标签= c（0,1）的R代码

    dataset <- read.csv("~/Desktop/Machine Learning /ML_16/Social_Network_Ads.csv")


dataset$Gender <- factor(dataset$Gender , levels = c("Female","Male") , labels = c(0 , 1))



library(caTools)

set.seed(1231)



sample_split <- sample.split(dataset$Gender , SplitRatio = 0.8)

training_dataset <- subset(dataset , sample_split == TRUE)

testing_dataset <- subset(dataset , sample_split == FALSE)


library(class)

model_classifier <- knn(train = training_dataset[,-5] , test = testing_dataset[,-5] , cl = training_dataset$Purchased , k = 21 )

library(caret)

confusionMatrix(table(model_classifier , testing_dataset$Purchased))

结果

Confusion Matrix and Statistics


model_classifier  0  1
               0 47 18
               1  4 11

Accuracy : 0.725

标签= c（0,10 ^ 6）

的R代码

dataset <- read.csv("~/Desktop/Machine Learning /ML_16/Social_Network_Ads.csv")


dataset$Gender <- factor(dataset$Gender , levels = c("Female","Male") , labels = c(0 , 10^6))



library(caTools)

set.seed(1231)



sample_split <- sample.split(dataset$Gender , SplitRatio = 0.8)

training_dataset <- subset(dataset , sample_split == TRUE)

testing_dataset <- subset(dataset , sample_split == FALSE)


library(class)

model_classifier <- knn(train = training_dataset[,-5] , test = testing_dataset[,-5] , cl = training_dataset$Purchased , k = 21 )

library(caret)

confusionMatrix(table(model_classifier , testing_dataset$Purchased))

结果

Confusion Matrix and Statistics


model_classifier  0  1
               0 50 23
               1  1  6

Accuracy : 0.7

标签究竟是什么？如果我们提供数字标签，它具有相同的数学意义

为什么KNN在R中更改因子标签时显示不同的预测

0 个答案: