Enquiry on model's settings
Hi,
As part of our journal article revision submission, I was asked by the journal article's reviewer to include the specific settings for each algorithm we used in RapidMiner's auto model (we used all except SVM).
We explained that we did not report them for easy understanding. However, we were told that the settings are very sensitive and important to report when machine learning is utilized. For instance, the number of neurons (or, hidden layers) in NN makes a huge difference in the prediction rate. In terms of gradient boosting, which condition (e.g., sci-kit learn, extreme boosting, etc) was utilized? In the case of a random forest, how many trees were set as the range to be selected?
While I look forward to insightful replies from community members here, I'd also welcome and would be more than happy if I could be referred to any resources or publication that can help me with this specific enquiry.
TIA
As part of our journal article revision submission, I was asked by the journal article's reviewer to include the specific settings for each algorithm we used in RapidMiner's auto model (we used all except SVM).
We explained that we did not report them for easy understanding. However, we were told that the settings are very sensitive and important to report when machine learning is utilized. For instance, the number of neurons (or, hidden layers) in NN makes a huge difference in the prediction rate. In terms of gradient boosting, which condition (e.g., sci-kit learn, extreme boosting, etc) was utilized? In the case of a random forest, how many trees were set as the range to be selected?
While I look forward to insightful replies from community members here, I'd also welcome and would be more than happy if I could be referred to any resources or publication that can help me with this specific enquiry.
TIA