I have a random forest model. With getTree function I can get all trees created in my random forest Model. Now I want to check the predictions made by each tree for some observations. For this reason I need to make prediction using each tree of my random forest Model.
I found this question with the same objective. But, unfortunately, this question has not been answered.
https://stackoverflow.com/q/40875489/3834837
Any propositions?
If you are referring to the randomForest package then you can do this by using predict(...,predict.all=T), which would get you all the predictions for each tree. Then you can select whichever you want.
Related
Good day,
for presentation purposes I would like to plot a couple of decision trees from a random forest (with about 100 trees). I found a post from last year where its clear is not really possible or there is not an function using tidymodels. R: Tidymodels: Is it possible to plot the trees for a random forest model in tidy models?
I´m wondering if somebody has found a way! I remember I could easily do this using the "Caret" package, but tidymodels makes everything so convenient I was hoping for someone with a solution.
Many thanks!
Summarizing what trees can be ploted with tidymodels based in comments comments and other Stackoverflow posts
Decision trees. There are some options but the function rpart.plot()seems to be the most popular.
Individual tree from a random forest. Doesn´t seem to be possible to plot one (yet) using the tidymodel environment. See this post: here
XGBoost models: See Julia comment:
You should be able to use a function like xgb.plot.tree() with a
trained tidymodels workflow or parsnip model by extracting out the
underlying object created with the xgboost engine. You can do this
with extract_fit_engine()
I'd like to use the 'e1071' library for fitting an SVM model. So far, I've made a model that creates a curve regression based on the data set.
(take a look at the purple curve):
However, I want the SVM model to "follow" the data, such that the prediction for each value is as close as possible to the actual data. I think this is possible because of this graph that shows how SVMs (model 2) model are similar to an ARIMA model (model 1):
I tried changing the kernel to no avail. Any help will be much appreciated.
Fine tuning a SVM classifier is no easy task. Have you considered other models? For ex. GAM's (generalized additive models)? These work well on very curvy data.
I am newbie in R and I need to know how to plot a tree selected from a random forest training model created using the train () function in caret package.
First and foremost, I used a training dataset to create a fitting model of a random forest using the train() function. The created random forest contains about 500 trees. Is there any methodology to create a plot of a selected tree?
Thank you.
CRAN package party offers a method called prettyTree.
Look here
As far as I know, the randomForest package does not have any built-in functionality to plot individual trees. You can extract trees using the getTree() function, but nothing is provided to plot / visualize it. This question may be a duplicate as a quick search yielded approaches other people have used to extract trees from a random forest are found
here and here and here
I'm using the "party" package to create random forest of regression trees.
I've created a ForestControl class in order to limit my number of trees (ntree), of nodes (maxdepth) and of variables I use to fit a tree (mtry).
One thing I'm not sure of is if the cforest algo is using subsets of my training set for each tree it generates or not.
I've seen in the documentation that it is bagging so I assume it should. But I'm not sure to understand well what the "subset" input is in that function.
I'm also puzzled by the results I get using ctree: when plotting the tree, I see that all my variables of my training set are classified in the different terminal tree nodes while I would have exepected that it only uses a subset here too.
So my question is, is cforest doing the same thing as ctree or is it really bagging my training set?
Thanks in advance for you help!
Ben
I've been using the caret package in R to run some boosted regression tree and random forest models and am hoping to generate prediction intervals for a set of new cases using the inbuilt cross-validation routine.
The trainControl function allows you to save the hold-out predictions at each of the n-folds, but I'm wondering whether unknown cases can also be predicted at each fold using the built-in functions, or whether I need to use a separate loop to build the models n-times.
Any advice much appreciated
Check the R package quantregForest, available at CRAN. It can easily calculate prediction intervals for random forest models. There's a nice paper by the author of the package, explaining the backgrounds of the method. (Sorry, I can't say anything about prediction intervals for BRT models; I'm looking for them by myself...)