How works the Random Forest Predictor?


I am trying to predict the values from the "Attr3_Prediction" using the data of the "Data 1" table.
For the prediction I am using the Random Forest Regression with the Learner and the Predictor.

I need an explanation what really happens in this nodes. Where happens the actual calculation?
How the predicted values will be filled in the "Attr3_Prediction" column?
Has the calculation a dependence on the "Attr1" and "Attr2" atibutes from the Data2?

The node desription was unfortunally not very helpful.

Thanks for help.


there are two references mentioned in the node description which could be a great starting point to understand exactly how the implemented algorithm works:

  • For two-class classification problems the method described in section 9.4 of "Classification and Regression Trees" by Breiman et al. (1984) is used.
  • For multi-class classification problems the method described in "Partitioning Nominal Attributes in Decision Trees" by Coppersmith et al. (1999) is used. 

I am afraid that these papers are quite technical though, so maybe not a good starting point if you have just started building your knowledge on the subject.

If you want to understand how a Random Forest algorithm works in simple terms, go on YouTube and search for "how does random forest work". You will find plenty of good explanations which will get you on the right track.

Hope this helps.