Can you get the relative influence of a GBM?
I agree that the gbm package doesn’t seem to allow for seeing the separate relative influence. If that’s something you’re interested in for a mutliclass problem, you could probably get something pretty similar by building a one-vs-all gbm for each of your classes and then getting the importance measures from each of those models.
How to print variable importance in GBM function?
N increasing N reduces the error on training set, but setting it too high may lead to over-fitting. interaction.depth (maximum nodes per tree) is number of splits it has to perform on a tree (starting from a single node).
How is the GBM function used in machine learning?
I am using the gbm function in R (gbm package) to fit stochastic gradient boosting models for multiclass classification. I am simply trying to obtain the importance of each predictor separately for each class, like in this picture from the Hastie book (the Elements of Statistical Learning) (p. 382).
How to get the importance of a variable?
For a multiclass problem there are actually n.trees*num.classes trees in the model. So if there are 3 classes you can calculate the sum of the ErrorReduction for each variable over every third tree to get the importance for one class. I have written the following functions to implement this and then plot the results:
How does the summary function work in GBM?
Applying the summary function to a gbm output produces both a Variable Importance Table and a Plot of the model. This table below ranks the individual variables based on their relative influence, which is a measure indicating the relative importance of each variable in training the model.
Is the gbm package in your used for multinomial classification?
I’m using the excellent gbm package in R to do multinomial classification, and my question is about feature selection.
Which is an important feature of the GBM model?
An important feature in the gbm modelling is the Variable Importance. Applying the summary function to a gbm output produces both a Variable Importance Table and a Plot of the model.