I recently used the
gbm package in RStudio for my analyis. All worked well. But I struggle to understand the summary of the model.
How to interpret the relative Influence of the variables? I can't find a definite answer to this question anywhere.
In this article from Towards Data Science I found this blurry description:
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. 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.
I got even more confused, when I read this paper about gradient boosting machines. They state in Chapter 5.1
Influences do not provide any explanations about how the variable actually affects the response. The resulting influences can then be used for both forward and backwards feature selection procedures.
Let's get specific with a small example:
Assume a model with 4 explanatory variables. The
gbm-model calculates relative importances as follows:
- variable1: 0.5
- variable2: 0.2
- variable3: 0.2
- variable4: 0.1
First of all, the model calculated no zero influence variables. that means, all variables are necessary.(?)
Can one say, variable1 explains 50% of variance?
Which statements can be made on the basis of this information?