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[R] Provide better guidance for persisting XGBoost model (#5964)

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* [R] Provide better guidance for persisting XGBoost model

* Update saving_model.rst

* Add a paragraph about xgb.serialize()
This commit is contained in:
Philip Hyunsu Cho
2020-07-31 20:00:26 -07:00
committed by GitHub
parent bf2990e773
commit 5a2dcd1c33
17 changed files with 233 additions and 82 deletions
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10
R-package/man/xgb.create.features.Rd
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@@ -24,9 +24,9 @@ This is the function inspired from the paragraph 3.1 of the paper:
\strong{Practical Lessons from Predicting Clicks on Ads at Facebook}
\emph{(Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yan, xin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers,
\emph{(Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yan, xin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers,
Joaquin Quinonero Candela)}
International Workshop on Data Mining for Online Advertising (ADKDD) - August 24, 2014
\url{https://research.fb.com/publications/practical-lessons-from-predicting-clicks-on-ads-at-facebook/}.
@@ -37,10 +37,10 @@ Extract explaining the method:
convenient way to implement non-linear and tuple transformations
of the kind we just described. We treat each individual
tree as a categorical feature that takes as value the
index of the leaf an instance ends up falling in. We use
1-of-K coding of this type of features.
index of the leaf an instance ends up falling in. We use
1-of-K coding of this type of features.
For example, consider the boosted tree model in Figure 1 with 2 subtrees,
For example, consider the boosted tree model in Figure 1 with 2 subtrees,
where the first subtree has 3 leafs and the second 2 leafs. If an
instance ends up in leaf 2 in the first subtree and leaf 1 in
second subtree, the overall input to the linear classifier will