Section 5.6.4 Disadvantages describes the benefit of using a monotonicity constraint for RuleFit, implying that RuleFit isn't a monotonic function. Below is the excerpt:
Sometimes RuleFit creates many rules that get a non-zero weight in the Lasso model. The interpretability degrades with increasing number of features in the model. A promising solution is to force feature effects to be monotonic, meaning that an increase of a feature has to lead to an increase of the prediction.
I apologize if this is obvious, but can you provide your reasoning/source for the emphasized text in the passage above?
In the passage, are you implying:
A general use of monotonicity constraint to improve the fit
AND/OR
Because RuleFit doesn't enforce any monotonicity constraint, you can improve the fit by imposing the constraint yourself
I have seen two conflicting references citing your book where the authors say RuleFit is and isn't monotonic. [1,2]
Reference 1
The author from reference 1 shows a table, where citation [12] in the screenshot below is
Section 5.6.4 Disadvantages describes the benefit of using a monotonicity constraint for RuleFit, implying that RuleFit isn't a monotonic function. Below is the excerpt:
I apologize if this is obvious, but can you provide your reasoning/source for the emphasized text in the passage above?
In the passage, are you implying:
I have seen two conflicting references citing your book where the authors say RuleFit is and isn't monotonic. [1,2]
Reference 1
The author from reference 1 shows a table, where citation [12] in the screenshot below is
Reference 2
The authors from reference 2 shows a table, where citation [70] in the screenshot below is
References