Contents
How are variables interacted in a random forest?
The operating definition of interaction used is that variables m and k interact if a split on one variable, say m, in a tree makes a split on k either systematically less possible or more possible. The implementation used is based on the gini values g (m) for each tree in the forest.
Is the random forest model an interpretable model?
Making random forest predictions interpretable is pretty straightforward, leading to a similar level of interpretability as linear models. However, in some cases, tracking the feature interactions can be important, in which case representing the results as a linear combination of features can be misleading.
How can we visualize random forest in R?
The methods therein can be found in the ‘randomForestSRC’ package. It should be noted that a recent paper indicated that identifying interactions, at least in the hyper-dimensional space of gene interactions, can be pretty tough: Can you help by adding an answer? How can we Visualize Random Forest in R?
How are Gini values used in random forest?
The implementation used is based on the gini values g (m) for each tree in the forest. These are ranked for each tree and for each two variables, the absolute difference of their ranks are averaged over all trees.
How to detect interaction between two dependent variables?
It is hard to identify interaction between two variables by looking at the variable importance plot. One way is to use the partial dependence plot. I was wondering if anyone has experience by using two variables at a time to detect interaction between them for the corresponding dependent variable.
How does mixed random forest work in mice?
In an application to data from an outbred mouse population, we find that mixed random forest identifies associations that are more consistent with prior knowledge than competing methods. Further, our approach allows predicting phenotypes from genotypes with greater accuracy than any of the other methods that we tested.
How to pip install mixed effects random forests in Python?
You can pip install our package off of PyPi by typing: The source code is available here. Contribute to it! The package is based on the excellent published work of Prof. Larocque from the Department of Decision Sciences at HEC and Prof. Ahlem from the Department of Marketing at l’UQAM.