The Altair Community is migrating to a new platform to provide a better experience for you. In preparation for the migration, the Altair Community is on read-only mode from October 28 - November 6, 2024. Technical support via cases will continue to work as is. For any urgent requests from Students/Faculty members, please submit the form linked here
Comparing kNN with SVM on Two Different Data Sets
Learner I
Hello,
I want to import two data sets, specifically the iris (which is found in the samples) and the wine data set (which I can import via Read CSV) and up to here, the problem with the wine data set is that instead of reading each attribute name separetly, e.g. "Alcohol", "Ash", "Phenols" etc, it combines all attribute names together in one name. In my CSV file, they are properly separated.
Next, I want to train on both of them with the kNN and SVM models using 10-fold validation and output each models Accuracy, Precision, Recall and F-Measure. The problem I have faced for this is that I used the "Cross Validation" operator and inside its parameters I have placed the kNN model (just to test for one model) and after I use the Apply Model operator on the Performance operator, I only get "accuracy" and "kappa" in the Criterion category. What I noticed is that If I use the "Titanic Training" dataset from the samples, it correctly outputs all the performance criterion that I have mentioned above (Precision, Recall etc). So why is that? Also, could I use the Compare ROCs operator to do it instead of Cross Validation?
I am circulating between youtube videos and the tutorials of Rapidminer for the last 8 hours to no avail. All I want to do is use the wine and iris datasets to compare the kNN classifier with the SVM classifier and output Accuracy, Precision, Recall and F-Measure. I am a newbie on Rapidminer, I really want to learn how to use it properly since it seems so polished and straightforward.
I want to import two data sets, specifically the iris (which is found in the samples) and the wine data set (which I can import via Read CSV) and up to here, the problem with the wine data set is that instead of reading each attribute name separetly, e.g. "Alcohol", "Ash", "Phenols" etc, it combines all attribute names together in one name. In my CSV file, they are properly separated.
Next, I want to train on both of them with the kNN and SVM models using 10-fold validation and output each models Accuracy, Precision, Recall and F-Measure. The problem I have faced for this is that I used the "Cross Validation" operator and inside its parameters I have placed the kNN model (just to test for one model) and after I use the Apply Model operator on the Performance operator, I only get "accuracy" and "kappa" in the Criterion category. What I noticed is that If I use the "Titanic Training" dataset from the samples, it correctly outputs all the performance criterion that I have mentioned above (Precision, Recall etc). So why is that? Also, could I use the Compare ROCs operator to do it instead of Cross Validation?
I am circulating between youtube videos and the tutorials of Rapidminer for the last 8 hours to no avail. All I want to do is use the wine and iris datasets to compare the kNN classifier with the SVM classifier and output Accuracy, Precision, Recall and F-Measure. I am a newbie on Rapidminer, I really want to learn how to use it properly since it seems so polished and straightforward.
Tagged:
0
Answers