你还是自己写段代码，运行一下confusionmatrix 自己看看就明白了。
不明白的话， 把运行结果截图上来，告诉大家哪里看不明白。

lz这个没有上下文的话，不好回答的。一般我了解的模型基本上都是lz所有的类型。但是作为例题，把有毒归为无毒要比把无毒归为有毒的问题严重的多，所以也确实会有所怀疑lz的理解是否有问题。所以最好还是lz自己把上下文发出来。

> str(mushroom)
'data.frame':        8124 obs. of  22 variables:
$ type           : Factor w/ 2 levels "e","p": 2 1 1 2 1 1 1 1 2 1 ...
$ cap_shape      : Factor w/ 6 levels "b","c","f","k",..: 6 6 1 6 6 6 1 1 6 1 ...
$ cap_surface    : Factor w/ 4 levels "f","g","s","y": 3 3 3 4 3 4 3 4 4 3 ...
$ cap_color      : Factor w/ 10 levels "b","c","e","g",..: 5 10 9 9 4 10 9 9 9 10 ...
$ bruises.       : Factor w/ 2 levels "f","t": 2 2 2 2 1 2 2 2 2 2 ...
$ odor           : Factor w/ 9 levels "a","c","f","l",..: 7 1 4 7 6 1 1 4 7 1 ...
$ gill_attachment: Factor w/ 2 levels "a","f": 2 2 2 2 2 2 2 2 2 2 ...
$ gill_spacing   : Factor w/ 2 levels "c","w": 1 1 1 1 2 1 1 1 1 1 ...
$ gill_size      : Factor w/ 2 levels "b","n": 2 1 1 2 1 1 1 1 2 1 ...
$ gill_color     : Factor w/ 12 levels "b","e","g","h",..: 5 5 6 6 5 6 3 6 8 3 ...
$ stalk_shape    : Factor w/ 2 levels "e","t": 1 1 1 1 2 1 1 1 1 1 ...
$ stalk_root     : Factor w/ 5 levels "?","b","c","e",..: 4 3 3 4 4 3 3 3 4 3 ...
$ ssar           : Factor w/ 4 levels "f","k","s","y": 3 3 3 3 3 3 3 3 3 3 ...
$ ssbr           : Factor w/ 4 levels "f","k","s","y": 3 3 3 3 3 3 3 3 3 3 ...
$ scar           : Factor w/ 9 levels "b","c","e","g",..: 8 8 8 8 8 8 8 8 8 8 ...
$ scbr           : Factor w/ 9 levels "b","c","e","g",..: 8 8 8 8 8 8 8 8 8 8 ...
$ veil_color     : Factor w/ 4 levels "n","o","w","y": 3 3 3 3 3 3 3 3 3 3 ...
$ ring_number    : Factor w/ 3 levels "n","o","t": 2 2 2 2 2 2 2 2 2 2 ...
$ ring_type      : Factor w/ 5 levels "e","f","l","n",..: 5 5 5 5 1 5 5 5 5 5 ...
$ spc            : Factor w/ 9 levels "b","h","k","n",..: 3 4 4 3 4 3 3 4 3 3 ...
$ population     : Factor w/ 6 levels "a","c","n","s",..: 4 3 3 4 1 3 3 4 5 4 ...
$ habitat        : Factor w/ 7 levels "d","g","l","m",..: 6 2 4 6 2 2 4 4 2 4 ...
> table(mushroom$type)   #根据蘑菇各种性状，判断蘑菇的type:有毒/无毒

   e    p
4208 3916

> library(RWeka)
> mushroom_1R=OneR(type~.,data = mushroom)  #使用1R算法判断蘑菇有毒无毒
> mushroom_1R
odor:
        a        -> e
        c        -> p
        f        -> p
        l        -> e
        m        -> p
        n        -> e
        p        -> p
        s        -> p
        y        -> p
(8004/8124 instances correct)

> summary(mushroom_1R)

=== Summary ===

Correctly Classified Instances        8004               98.5229 %
Incorrectly Classified Instances       120                1.4771 %
Kappa statistic                          0.9704
Mean absolute error                      0.0148
Root mean squared error                  0.1215
Relative absolute error                  2.958  %
Root relative squared error             24.323  %
Total Number of Instances             8124     

=== Confusion Matrix ===

    a    b   <-- classified as
4208    0 |    a = e
  120 3796 |    b = p

对下面的混淆矩阵存疑

你好，已经加上了，望解答，谢谢

你好已经加上了