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Gradient boosting is a machine learning technique based on boosting in a functional space, where the target is pseudo-residuals instead of residuals as in traditional boosting. It gives a prediction model in the form of an ensemble of weak prediction models, i.e., models that make very few assumptions about the data, which are typically simple decision…
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displaystyle gradient boosting model function algorithm tree set training trees gamma base loss decision regularization regression number weak learning learner
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Gradient boosting | is a | machine learning technique based on boosting in a functional space | 0.90 | text |
| an ℓ 2 | instance of | The joint optimization of loss and model complexity corresponds to a post-pruning algorithm to remove branches that fail to reduce the loss by a threshold.Other kinds of regular… | 0.80 | text |
| Gradient boosting | related to Algorithm | Many | 0.60 | section |
| Gradient boosting | related to Algorithm | The | 0.60 | section |
| Gradient boosting | related to Algorithm | This | 0.60 | section |
| Gradient boosting | related to Algorithm | It | 0.60 | section |
| Gradient boosting | related to External links | How | 0.60 | section |
| Gradient boosting | related to External links | Boosted Regression TreesLightGBM | 0.60 | section |
| Gradient boosting | related to Feature importance ranking | Gradient | 0.60 | section |
| Gradient boosting | related to Feature importance ranking | For | 0.60 | section |
| Gradient boosting | related to Further reading | Boehmke | 0.60 | section |
| Gradient boosting | related to Further reading | Bradley | 0.60 | section |
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