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I receive the following comment from a reviewer : "I think that the authors could explain in more details the results. For instance, there is no discussion linking the specific properties of the datasets at hand and the performance of the classification methods".

To be honest I did not understand what the reviewer said. What are the specific properties of the dataset?

Is it possible just by looking at those specific properties to infer which classifiers will have a better performance?

Is it possible to explain the performance of the classifiers by the specific properties of the data sets?

Adding more details to my question. Suppose that classifier A has the following results in UCI datasets ( the performance measure is the accuracy in tem fold cross validation with 5 repetitions )

  1. Iris : 96%
  2. Wine: 93%
  3. Sonar:77%
  4. Pima: 75%
  5. balance: 84%
  6. Habeman:76%
  7. Breast: 96%
  8. Australian: 84%
  9. Ionosfere: 90%
  10. Lupus: 75%
  11. Bupa: 65%
  12. Transfusion: 77%
  13. Lawsuit: 96%

Classifier A presented good performance in Iris, Wine, Ionosfere and lawsuit. Is there any specific properties in commom with these datasets? Or Are they all just considered "easy" datasets?

I receive the following comment from a reviewer : "I think that the authors could explain in more details the results. For instance, there is no discussion linking the specific properties of the datasets at hand and the performance of the classification methods".

To be honest I did not understand what the reviewer said. What are the specific properties of the dataset?

Is it possible just by looking at those specific properties to infer which classifiers will have a better performance?

Is it possible to explain the performance of the classifiers by the specific properties of the data sets?

I receive the following comment from a reviewer : "I think that the authors could explain in more details the results. For instance, there is no discussion linking the specific properties of the datasets at hand and the performance of the classification methods".

To be honest I did not understand what the reviewer said. What are the specific properties of the dataset?

Is it possible just by looking at those specific properties to infer which classifiers will have a better performance?

Is it possible to explain the performance of the classifiers by the specific properties of the data sets?

Adding more details to my question. Suppose that classifier A has the following results in UCI datasets ( the performance measure is the accuracy in tem fold cross validation with 5 repetitions )

  1. Iris : 96%
  2. Wine: 93%
  3. Sonar:77%
  4. Pima: 75%
  5. balance: 84%
  6. Habeman:76%
  7. Breast: 96%
  8. Australian: 84%
  9. Ionosfere: 90%
  10. Lupus: 75%
  11. Bupa: 65%
  12. Transfusion: 77%
  13. Lawsuit: 96%

Classifier A presented good performance in Iris, Wine, Ionosfere and lawsuit. Is there any specific properties in commom with these datasets? Or Are they all just considered "easy" datasets?

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Is it possible to explain the performance of a classifier by the specific properties of a data set?

I receive the following comment from a reviewer : "I think that the authors could explain in more details the results. For instance, there is no discussion linking the specific properties of the datasets at hand and the performance of the classification methods".

To be honest I did not understand what the reviewer said. What are the specific properties of the dataset?

Is it possible just by looking at those specific properties to infer which classifiers will have a better performance?

Is it possible to explain the performance of the classifiers by the specific properties of the data sets?