set.seed(1)library(caret)library(dslabs)library(dplyr)data("tissue_gene_expression")y_<-tissue_gene_expression$ytest_index <- createDataPartition(y_, times = 1, ,p=0.5, list = FALSE)x_train<-tissue_gene_expression$x[-test_index,]y_train<-tissue_gene_expression$y[-test_index]x_test<-tissue_gene_expression$x[test_index,]y_test<-tissue_gene_expression$y[test_index]fit<-knn3(x_train,y_train,k=1)y_test_hat<-predict(fit,x_test,type = 'class')F_meas(data = y_test_hat,reference = y_test)以上是我的代码，但它总是返回以下错误：
Error in F_meas.default(data = y_test_hat, reference = y_test, ) : input data must have the same two levels尽管我已经检查了这两个数据（y_test_hat 和 y_test_hat）的级别，它们都有相同的7个级别
回答：
在这种情况下，”two”指的是2，而不是7。因此，要正确使用F_meas，你需要重新排列你的数据，例如cerebellum, colon, endometrium, hippocampus, kidney, liver, placenta，并为F_meas提供两个级别，否则它将无法工作。
#' @rdname recall#' @importFrom stats complete.cases#' @exportrecall.default <- function(data, reference, relevant = levels(reference)[1], na.rm = TRUE, ...) { if (!is.factor(reference) | !is.factor(data)) stop("input data must be a factor") if (length(unique(c(levels(reference), levels(data)))) != 2) stop("input data must have the same two levels") # 这里我们看到two指的是2 if (na.rm) {cc <- complete.cases(data) & complete.cases(reference) if (any(!cc)) { data <- data[cc] reference <- reference[cc]}} xtab <- table(data, reference) recall.table(xtab, relevant = relevant)} 希望这能澄清问题。
