Greedy Function Approximation¶
Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189-1232.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Residual Analysis
- Medicine: a clinician's leading hypothesis is the model, and the symptoms it fails to explain are the residual that drives differential-diagnosis refinement. Machine learning: gradient boosting fits successive weak learners to the residuals of prior learners; residual connections instantiate the move at the architecture level.
This sourceFormalizes gradient boosting as the fitting of successive weak learners to the residuals of prior learners, converging when residuals stop carrying exploitable pattern.
Supported in partVerified against the work's full text
Friedman shows gradient boosting fits successive base learners to current residuals (exactly so for squared-error loss); the medicine example and residual connections are not covered.
“gradient boosting on squared-error loss produces the usual stagewise approach”
- Medicine: a clinician's leading hypothesis is the model, and the symptoms it fails to explain are the residual that drives differential-diagnosis refinement. Machine learning: gradient boosting fits successive weak learners to the residuals of prior learners; residual connections instantiate the move at the architecture level.
Domain-specific¶
Verification¶
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Registry ID ref:64267c338b0b · see in the full table