什么地方出了错？ 错误：`data` 和 `reference` 应该是具有相同水平的因素

Question

> # Check results > model_knn$results k Accuracy Kappa AccuracySD KappaSD 1 5 0.9632391 0.9439746 0.02452539 0.03727995 2 7 0.9699800 0.9544974 0.02451292 0.03708112 3 9 0.9677304 0.9509734 0.02617121 0.03986928 > # Predict the labels of the test set > predictions<-predict.train(object=model_knn,iris_norm.test[,1:4], type="raw") > > # Evaluate the predictions > table(predictions) predictions Iris-setosa Iris-versicolor Iris-virginica 12 14 10 > > #confusion matrix > > # ENTER YOUR CODE HERE > confusionMatrix(predictions,iris_norm.test[,5]) Error: `data` and `reference` should be factors with the same levels.

Answer 1

Without the model or data to reproduce your case, I can only suggest to align the factor levels using forcats::fct_unify before passing the two vectors into confusionMatrix :

library(forcats)
library(caret)

do.call(
  confusionMatrix,
  fct_unify(list(
    data = predictions,
    reference = iris_norm.test[,5]
  ))
)

fct_unify works on a list of factor vectors and makes sure they all share the same set of levels. Constructing that list with names corresponding to the expected arguments of confusionMatrix , I can pass it right into with do.call .