prediction nodes and selection features

hello dear form users

ı have a question ı m new to knime and datascience. ı want to prediction so ı want to using Desicion Tree Regressor, Random Forest Regressor , Support Vector Regressor , Multiple Lineaar regressor and Gainet Boosting Tree Algorithm.

What are the nodes of these algorithms?

and I need to select a feature using back-elimination or forward-elimination methods.
So where can I find the p-value values?

Hello BerkeyAkar,

In order to use these nodes in your Knime platform, you should install the relevant extensions from “”.
Please follow these steps:
1- go to “”.
2- search the node you are looking for, for example: “decision tree”.
3- select “decision tree (category)”
4- In the new window, scroll down and find “Installation”. Under this topic, copy the name of the specific extension, here for decision tree learner node we have " KNIME Base nodes".
5- open your knime platform, click on File —> Install Knime Extensions —> paste "“Knime Base nodes” on search box. When you find it, select it and then follow the instructions to install it.

I hope this will be helpful for you. Please let me know if there is any further information I can provide.



KNIME Base nodes Extension does not need to be installed separately as it’s included upon KNIME installation. And if not mistaken provides nodes for all above mentioned algorithms. @BerkayAkar have you tried searching these nodes in Node Repository (e.g. decision tree...)? That was my first try when starting with KNIME and looking for certain functionality or algorithm. Also I was exploring workflow examples on EXAMPLES Server a lot. You can access those from KNIME Explorer or search for them on KNIME Hub.

Additionally as you are new to KNIME and data science I suggest you explore KNIME learning materials. Especially Data Science courses. Good luck!



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