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I am a beginner in KNIME and I need to predict attribute I have some questions :

1- How can I choose the most related attribute to predict the target attribute?

2- can I choose the attributes randomly then compare the accuracy?

3- can I use nominal attributes or only numerical attributes?

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1) You don't, at least initially. Use all of the variables you have, and through proper cross validation/model tuning (in particular, tuning the number of variables you randomly select to try in each split of the tree, and maybe stuff like minimum node size and maximum size of individual trees) let the random forest find the most predictive variables for you.

If you have a lot of candidate variables, then maybe try an wrapper based method like permutation importance, recursive feature elimination, etc. though for now I'd avoid this especially if you are a beginner (these methods tend to take a long time, and may not be useful/offer marginal improvement).

2) What is the purpose of this? As a means of feature selection? Random forests already kind of do this; they randomly select a subset of your variables at each split in a tree. Again, I would stick to letting the random forest do its feature selection for you for now.

3) Both. Nominal attributes are often one-hot encoded/dummy encoded (create k-1 dummy columns, k = number of categories in the nominal variable) which I encourage you to search up. Basically, you are encoding a nominal attribute into a numerical one.

You can also encode nominal variables through other means (like target encoding) but again, if you are a beginner let's avoid that for now because you can severely screw up your model validation by doing this.

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  • $\begingroup$ do I have to use the decision tree node first? $\endgroup$ – user75273 Jun 1 at 18:27
  • $\begingroup$ Decision tree node? Is this kNIME functionality or something? I've never used kNIME before, but in general, when people refer to a "decision tree" they are referring only to a single tree only. A random forest (in the most simplest sense) is an ensemble of many of these decision trees combined together through averaging. Otherwise, I am not too sure what you exactly mean here. $\endgroup$ – aranglol Jun 1 at 19:53

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