我已经使用svm的“rfe”函数来创建具有减少功能的模型。然后我在测试数据上使用“预测”，输出类标签（二进制），0类概率，1类概率。然后我尝试在ROCR包中使用预测函数预测概率和真实类标签，但得到以下错误并且不确定为什么因为2个数组的长度相等：
> pred_svm <- prediction(pred_svm_2class[,2], as.numeric(as.character(y)))
Error in prediction(pred_svm_2class[, 2], as.numeric(as.character(y))) :
Number of predictions in each run must be equal to the number of labels for each run.
我有下面的代码，输入在这里click me.It是一个二进制分类的小数据集，因此代码运行速度很快。
library("caret")
library("ROCR")
sensor6data_2class <- read.csv("/home/sensei/clustering/svm_2labels.csv")
sensor6data_2class <- within(sensor6data_2class, Class <- as.factor(Class))
set.seed("1298356")
inTrain_svm_2class <- createDataPartition(y = sensor6data_2class$Class, p = .75, list = FALSE)
training_svm_2class <- sensor6data_2class[inTrain_svm_2class,]
testing_svm_2class <- sensor6data_2class[-inTrain_svm_2class,]
trainX <- training_svm_2class[,1:20]
y <- training_svm_2class[,21]
ctrl_svm_2class <- rfeControl(functions = rfFuncs , method = "repeatedcv", number = 5, repeats = 2, allowParallel = TRUE)
model_train_svm_2class <- rfe(x = trainX, y = y, data = training_svm_2class, sizes = c(1:20), metric = "Accuracy", rfeControl = ctrl_svm_2class, method="svmRadial")
pred_svm_2class = predict(model_train_svm_2class, newdata=testing_svm_2class)
pred_svm <- prediction(pred_svm_2class[,2], y)
谢谢，感谢您的帮助。
这是因为在线
pred_svm <- prediction(pred_svm_2class[,2], y)
pred_svm_2class [，2]是对测试数据的预测，y是训练数据的标签。只需在像这样的单独变量中生成测试标签
y_test <- testing_svm_2class[,21]
现在，如果你这样做
pred_svm <- prediction(pred_svm_2class[,2], y_test)
没有错误。完整代码如下 -
# install.packages("caret")
# install.packages("ROCR")
# install.packages("e1071")
# install.packages("randomForest")
library("caret")
library("ROCR")
sensor6data_2class <- read.csv("svm_2labels.csv")
sensor6data_2class <- within(sensor6data_2class, Class <- as.factor(Class))
set.seed("1298356")
inTrain_svm_2class <- createDataPartition(y = sensor6data_2class$Class, p = .75, list = FALSE)
training_svm_2class <- sensor6data_2class[inTrain_svm_2class,]
testing_svm_2class <- sensor6data_2class[-inTrain_svm_2class,]
trainX <- training_svm_2class[,1:20]
y <- training_svm_2class[,21]
y_test <- testing_svm_2class[,21]
ctrl_svm_2class <- rfeControl(functions = rfFuncs , method = "repeatedcv", number = 5, repeats = 2, allowParallel = TRUE)
model_train_svm_2class <- rfe(x = trainX, y = y, data = training_svm_2class, sizes = c(1:20), metric = "Accuracy", rfeControl = ctrl_svm_2class, method="svmRadial")
pred_svm_2class = predict(model_train_svm_2class, newdata=testing_svm_2class)
pred_svm <- prediction(pred_svm_2class[,2], y_test)