I'm quite new with KNIME. I recently took Data Mining course and my faculty gave me project on "GERMAN DATA SET"
I ran SVM(Support Vector Machine) without any feature subset selection on my dataset. I've built a project with the classical nodes (file reader, partitioning , SVM learner,SVM predictor, scorer) and executed. My accuracy is coming around 75%.
I tried Low variance filter and PCA for feature subset selection but my accuracy is decreasing rather than increasing.
Can you suggest any Feature Subset Selection mechanism by which i can increase my accuracy?
Other methods for increasing accuracy will be highly appreciated?
What about the Feature Elimination metanode?
Can you tell me what should be the input and output node for feature Elimination metanode
Did you have a look at the metanode? It has a short description in it. The input is the dataset the output depends on which features you have selected in the feature elimination filter node.
HI thor, we have implemented till meta node , (i replaced naive with my svm). now how to use the output of this meta node to classify data by svm?
2nd Output of meta node is giving filtered table. Now how to get classified data to apply on the scorer node?
Where is the predicted column to compare at scorer?
The second output is the second input but only the relevant columns (which you have selected inside the metanode). You can use the second output to train any model (ideally the same as you used in the feature elimination loop) and use KNIME's standard learner-predictor-scorer schema. The metanode itself does not do the final prediction or scoring.