# Intepreting random forest model output **URL:** \ **Category:** KNIME Analytics Platform\ **Created:** [October 16, 2020, 2:08pm UTC](https://forum.knime.com/t/intepreting-random-forest-model-output/28041 "2020-10-16T14:08:12Z")\ **Posts on this page:** 3\ **Page:** 1
**Author:** ![Sabina](https://forum-cdn.knime.com/letter_avatar_proxy/v4/letter/s/bb73d2/32.png) [@Sabina](https://forum.knime.com/u/Sabina)\ **Post date:** [October 16, 2020, 2:08pm UTC](https://forum.knime.com/t/intepreting-random-forest-model-output/28041/1 "2020-10-16T14:08:12Z")
Hello, I am a relatively new user to KNIME, and I built a random forest predictor (regression) model in KNIME where my predictor (or independent) variable is binary (1,0) and my outcome (i.e. dependent) variable is ordinal with values 0 to 10. For my binary variable, “1” means it is present, and “0” means it is absent. I want to know what the predicted outcome would be from the random forest model if my predictor variable is a 1 i.e. present. In the “prediction Output” table, should I only look at the predicted values that correspond to a “1” in my predictor variable? Thank you in advance ---
**Author:** ![Daniel\_Weikert](https://forum-cdn.knime.com/letter_avatar_proxy/v4/letter/d/e47c2d/32.png) [@Daniel\_Weikert](https://forum.knime.com/u/Daniel_Weikert)\ **Post date:** [October 17, 2020, 1:31pm UTC](https://forum.knime.com/t/intepreting-random-forest-model-output/28041/2 "2020-10-17T13:31:30Z")
You could use new data and predict to see. It looks like you have a multilabel classification task. Why then using a regression? ---
**Author:** ![system](https://forum-cdn.knime.com/uploads/default/original/1X/ab3ccf34482a0329361734a18199390177204f15.png) [@system](https://forum.knime.com/u/system)\ **Post date:** [April 18, 2021, 1:32am UTC](https://forum.knime.com/t/intepreting-random-forest-model-output/28041/3 "2021-04-18T01:32:30Z")
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