Question

我，关于每次观察的概率输出格式的问题。

Current output:
                0            1
2282 9.791608e-01 2.083920e-02 
135  4.769759e-01 5.230241e-01 
2036 9.807866e-01 1.921336e-02

Desired output:                      just like the example below produces
           1         0
9  0.4268682 0.5731318
10 0.4268682 0.5731318
4  0.4268682 0.5731318
7  0.2590067 0.7409933
2  0.2590067 0.7409933

通过下面的可重复示例，我得到所需的输出为0到1之间的概率。但是，当我用另一个更大的数据集运行完全相同的代码时，我想运行我的分析，包含1100个变量和10000个观测值每个单元格都填充0或1，所以相同的数据集更大，然后我得到如上所示的当前输出。

# data preparation
A <- c(1, 0, 1, 1, 0, 0, 0, 1, 0, 0) 
B <- c(1,0,1,1,1,1,0,0,1,1) 
C <- c(0,1,1,1,1,1,1,1,1,1) 
train <- data.frame(A, B, C) 
train[] <- lapply(train, as.factor)


# randomize data
train <- train[sample(nrow(train)),]


# create 10 equal folds
number_folds <- 2
folds <- cut(seq (1, nrow(train)), breaks = number_folds, labels = FALSE)


# Vectors created to store the initialized values with 0’s
accuracy_SVM <- rep(0,number_folds)


# install packages required for SVM and NB
install.packages("e1071")
library("e1071")


# Cross validation, data segmentations and running the model
for(i in 1:number_folds){
  testIndexes <- which(folds == i, arr.ind = TRUE)
  testData <- train[testIndexes ,]
  trainData <- train[-testIndexes ,]

SVM_model <- svm(A ~ ., data = trainData, probability = T)
classification_svm <- predict(SVM_model, testData, type ="response", probability = T)
accuracy_SVM[i] <- sum(classification_svm == testData$A) / nrow (testData)
}

attr(classification_svm, "probabilities")

很抱歉，我无法提供自己的数据集来帮助您复制相同的输出，但我无法更清楚地表达问题。非常感谢帮助！：）

0到1 svm r

0 个答案: