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.
One thing to try would be using a smaller value for Leaf Fraction - could be too high to find splits.
May I know what is leaf fraction and why we need to use a smaller for it?
May I know what is leaf fraction in Gradient Boosting?
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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