machine learning - Aggregating labels in GradientBoostingRegression -

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i trying understand scikit-learn's gradient boosting regression algorithm. followed source code , have understanding of iterative construction of trees based on chosen loss function. couldn't figure out answer how take average of labels underlying estimators when invoke predict() .

i followed function call down this line. here, scale holds learning_rate , if not provided, default 0.1. so, if use 500 trees, don't understand why multiplying each of 500 different labels, given sample, 0.1.

if direct me published paper explains in depth, appreciated.


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