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How to compare the SVM and random forest results?
I am trying to use the prediction in Auto Model but encountered several questions on the results of SVM and random forest.
- I wonder why the results of SVM and RF barely match? For example, attribute 1 has the highest weight based on SVM result, but it became one of the attributes having the lowest weight in the RF result.
- Why the weights of several attributes are 0 in SVM? Is it possible that the attributes have 0 weight to the model even though I only selected those attributes marked as "green" in input selection? But this did not happen in RF.
- Continuing with question 2, I tried to play with the data marked as orange and red in input selection. I discovered that an attribute could have the highest weight in the case that I selected all of them (regardless of green, orange, and red). However, that attribute is actually marked as RED. Why does this happen? In this case, would you suggest me to include all attributes in case there is any important attribute that actually contribues to the model a lot?