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You might want to have a look at the following examples:
Basically, a |
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Can someone please put the reference of the multi-output random forest model? I mean the link to the original scientific paper. |
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@shayandavoodii classic references is Segal, Mark, and Yuanyuan Xiao. "Multivariate random forests." |
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Hi all,
I have a doubt regarding Random Forests Regression.
I have multiple input features for training and the corresponding multiple output features for predicting.
My question is how does the model fit on data?
Could someone please explain in simple words
Like for an example, if we have 3 input variable (independent variable) based on which, when the model is trained, we predict one output variable (dependent variable). A mathematical expression for that would be " Y_hat = (w_1 * X_1) + (w_2 * X_2) + (w_3 * X_3) + Error"
How does it work in predicting multiple outputs, specifically what approach does sklearn.ensemble.RandomForestRegressor take?
Thanks in Advance!!!!!
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