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aha123
Obsidian | Level 7

For my dataset, I run Regression, Neural Network,  Decision Trees & Gradient Boosting Nodes. GB node always has the highest MSE, even higher than Regression. Even after I changed N interations to 200, it is still the worst one. Could you suggest what other parameters I can try out to improve the outcome? I run my dataset on R & Python, GB from both give very good results. Don't know what is wrong with SAS's GB node. My problem is of regression, not of classification.

4 REPLIES 4
WendyCzika
SAS Employee

One thing to try would be using a smaller value for Leaf Fraction - could be too high to find splits.

Babloo
Rhodochrosite | Level 12

May I know what is leaf fraction and why we need to use a smaller for it?

Babloo
Rhodochrosite | Level 12

May I know what is leaf fraction in Gradient Boosting?

WendyCzika
SAS Employee

Use the Leaf Fraction property to specify the smallest number of training observations a new branch may have, expressed as the proportion of the number N of available training observations in the data. N may be less than the total number of observations in the data set because observations with a missing target value are excluded.

 

So setting this to a smaller value grows larger trees.

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