Weka : How to prepare test set in weka -


i have been using svm classifier following data

@relation whatever  @attribute mfe numeric @attribute gb numeric @attribute gtb numeric @attribute seeds numeric @attribute abp numeric @attribute au_seed numeric @attribute gc_seed numeric @attribute gu_seed numeric @attribute numeric @attribute au numeric @attribute gc numeric @attribute gu numeric @attribute a-u_l numeric @attribute g-c_l numeric @attribute g-u_l numeric @attribute (g+c) numeric @attribute mfei1 numeric @attribute mfei2 numeric @attribute mfei3 numeric @attribute mfei4 numeric @attribute dg numeric @attribute dp numeric @attribute dq numeric @attribute dd numeric @attribute outcome {yes,no}   @data -24.3,1,18,2,9,4,3,0.5,8,10,7,1,0.454545455,0.318181818,0.045454545,7,-0.157792208,-0.050206612,-1.104545455,-1.35,-1.104545455,0,0,0,yes -24.8,2,15,2,7.5,2,3,1,7,5,8,2,0.208333333,0.333333333,0.083333333,8,-0.129166667,-0.043055556,-0.516666667,-1.653333333,-1.033333333,0,0,0,no -24.4,1,16,3,5.333333333,1.666666667,2.666666667,1,4,5,8,3,0.217391304,0.347826087,0.130434783,8,-0.132608696,-0.046124764,-1.060869565,-1.525,-1.060869565,0,0,0,yes -24.2,1,18,2,9,2,2.5,1,10,5,11,2,0.227272727,0.5,0.090909091,11,-0.1,-0.05,-1.1,-1.344444444,-1.1,0,0,0,yes -24.5,3,17,2,8.5,2,3,1,5,6,9,2,0.272727273,0.409090909,0.090909091,9,-0.123737374,-0.050619835,-0.371212121,-1.441176471,-1.113636364,-0.12244898,0,0,yes 

this training set . , in defined whether data yes class or no class. question test data unknown source , dont have idea class belongs. how prepare test set. without outcome attribute weka giving "ereor: data mismatch " . how prepare test set? separate variable yes , no class using svm.

steps prepare test set:

  1. create training set in csv format.
  2. also create test set in csv format same no. of attributes , same type.
  3. copy test set , paste @ end of training set , save new csv file.
  4. import saved csv file in step 3 using weka>>explorer>>preprocess.
  5. in filter option choose filters>>unsupervised>>instances>>remove range.
  6. click feed says removerange-r first-last.
  7. specify range want remove training data had 100 values, select first-100 , apply filter.
  8. save arff file , can used test set.
  9. then apply set. if still have errors, write reply post.

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