从数据帧创建混淆矩阵 [英] Create a confusion matrix from a dataframe

问题描述

我有这个数据框架叫做 conf_mat ，其中包含每个对象中的预测值和参考值两列。我在这个数据框中有20个对象。

I have this data frame called conf_mat with two columns including predicted values and reference values in each objects. I have 20 objects in this dataframe.

 dput(Conf_mat)
structure(list(Predicted = c(100, 200, 200, 100, 100, 200, 200, 
200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200
), Reference = c(600, 200, 200, 200, 200, 200, 200, 200, 500, 
500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200)), .Names = c("Predicted", 
"Reference"), row.names = c(NA, 20L), class = "data.frame")

我想用这种结构创建一个混合矩阵，这个结构将由 Conf_mat 数据框填充。这将允许我计算我的分类的准确性评估。谢谢你的帮助。

I want to create a confusion matrix out of this table with this kind of structure which will be filled in by the Conf_mat dataframe. This will be allow me to compute an accuracu assessment of my classification. Thanks for your help.

    100 200 300 400 500 600
100  NA  NA  NA  NA  NA  NA
200  NA  NA  NA  NA  NA  NA
300  NA  NA  NA  NA  NA  NA
400  NA  NA  NA  NA  NA  NA
500  NA  NA  NA  NA  NA  NA
600  NA  NA  NA  NA  NA  NA

推荐答案

1）尝试以下操作： p>

1) Try the following:

table(Conf_mat)

2）如果要强制等级100,200，...，600出现：

2) If you want to force levels 100, 200, ..., 600 to appear:

conf_mat_tab <- table(lapply(Conf_mat, factor, levels = seq(100, 600, 100)))

3）您还可以尝试以下方式：

3) You could also try this:

library(caret)
confusionMatrix(conf_mat_tab) # conf_mat_tab from (2)

其中： p>

which gives:

Confusion Matrix and Statistics

         Reference
Predicted 100 200 300 400 500 600
      100   0   9   0   0   1   1
      200   0   6   0   0   1   0
      300   0   0   0   0   0   0
      400   0   0   0   0   0   0
      500   0   1   0   0   1   0
      600   0   0   0   0   0   0

Overall Statistics

               Accuracy : 0.35            
                 95% CI : (0.1539, 0.5922)
    No Information Rate : 0.8             
    P-Value [Acc > NIR] : 1               

                  Kappa : 0.078           
 Mcnemar's Test P-Value : NA              

Statistics by Class:

                     Class: 100 Class: 200 Class: 300 Class: 400 Class: 500 Class: 600
Sensitivity                  NA     0.3750         NA         NA     0.3333       0.00
Specificity                0.45     0.7500          1          1     0.9412       1.00
Pos Pred Value               NA     0.8571         NA         NA     0.5000        NaN
Neg Pred Value               NA     0.2308         NA         NA     0.8889       0.95
Prevalence                 0.00     0.8000          0          0     0.1500       0.05
Detection Rate             0.00     0.3000          0          0     0.0500       0.00
Detection Prevalence       0.55     0.3500          0          0     0.1000       0.00
Balanced Accuracy            NA     0.5625         NA         NA     0.6373       0.50

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