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I wanted to know that does learning rate impact the model building time in case of Gradient Boosted Trees. I do understand that increasing the number of trees have an impact( more the trees, more the time) however I am not sure about the learning rate.

For example, if I train my model with learning_rate = [0.1,0.2,0.3] how do these values rank up in terms of building time.

My understanding is that the lower the learning rate, the more the time gradient descent takes to converge and hence more building time. However, after reading Q23 from the below link, I am confused.

https://www.analyticsvidhya.com/blog/2017/09/30-questions-test-tree-based-models/

Can anyone please share some insight on this.

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  • $\begingroup$ Maybe you find this link useful: towardsdatascience.com/… $\endgroup$ – JonnyCrunch Feb 11 at 15:07
  • $\begingroup$ Oh I read that link, before posting my question here. I understand the working in case of deep learning , however is that also applicable in Gradient Boosted Trees? $\endgroup$ – Shekhar Tanwar Feb 11 at 15:08

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