R:多类矩阵 [英] R: Multiclass Matrices
问题描述
我正在使用R编程语言.我正在尝试学习如何制作一个混淆矩阵".用于多类变量(例如如何构造混淆多类变量的矩阵).
I am working with the R programming language. I am trying to learn how to make a "confusion matrix" for multiclass variables (e.g. How to construct the confusion matrix for a multi class variable).
假设我生成了一些数据并拟合了决策树模型:
Suppose I generate some data and fit a decision tree model :
#load libraries
library(rpart)
library(caret)
#generate data
a <- rnorm(1000, 10, 10)
b <- rnorm(1000, 10, 5)
d <- rnorm(1000, 5, 10)
group_1 <- sample( LETTERS[1:3], 1000, replace=TRUE, prob=c(0.33,0.33,0.34) )
e = data.frame(a,b,d, group_1)
e$group_1 = as.factor(d$group_1)
#split data into train and test set
trainIndex <- createDataPartition(e$group_1, p = .8,
list = FALSE,
times = 1)
training <- e[trainIndex,]
test <- e[-trainIndex,]
fitControl <- trainControl(## 10-fold CV
method = "repeatedcv",
number = 5,
## repeated ten times
repeats = 1)
#fit decision tree model
TreeFit <- train(group_1 ~ ., data = training,
method = "rpart2",
trControl = fitControl)
从这里,我可以将结果存储到混淆矩阵"中:
From here, I am able to store the results into a "confusion matrix":
pred <- predict(TreeFit,test)
table_example <- table(pred,test$group_1)
这满足了我的要求-但此表"要求我手动计算"A","B"的不同精度度量.和"C"表示(以及总准确度).
This satisfies my requirements - but this "table" requires me to manually calculate the different accuracy metrics of "A", "B" and "C" (as well as the total accuracy).
我的问题:是否可以使用 caret :: confusionMatrix()
命令解决此问题?
My question: Is it possible to use the caret::confusionMatrix()
command for this problem?
例如
pred <- predict(TreeFit, test, type = "prob")
labels_example <- as.factor(ifelse(pred[,2]>0.5, "1", "0"))
con <- confusionMatrix(labels_example, test$group_1)
这样,我将能够直接从混淆矩阵中访问准确性测量值.例如. metric = con $ overall [1]
This way, I would be able to directly access the accuracy measurements from the confusion matrix. E.g. metric = con$overall[1]
谢谢
推荐答案
这是您要寻找的吗?
pred <- predict(
TreeFit,
test)
con <- confusionMatrix(
test$group_1,
pred)
con
con$overall[1]
与以下输出相同:
table(test$group_1, pred)
加上准确性指标.
这篇关于R:多类矩阵的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持IT屋!