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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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16
R-package/man/xgb.importance.Rd
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@@ -22,7 +22,7 @@ Non-null \code{feature_names} could be provided to override those in the model.}
\item{trees}{(only for the gbtree booster) an integer vector of tree indices that should be included
into the importance calculation. If set to \code{NULL}, all trees of the model are parsed.
It could be useful, e.g., in multiclass classification to get feature importances
It could be useful, e.g., in multiclass classification to get feature importances
for each class separately. IMPORTANT: the tree index in xgboost models
is zero-based (e.g., use \code{trees = 0:4} for first 5 trees).}
@@ -37,7 +37,7 @@ For a tree model, a \code{data.table} with the following columns:
\itemize{
\item \code{Features} names of the features used in the model;
\item \code{Gain} represents fractional contribution of each feature to the model based on
the total gain of this feature's splits. Higher percentage means a more important
the total gain of this feature's splits. Higher percentage means a more important
predictive feature.
\item \code{Cover} metric of the number of observation related to this feature;
\item \code{Frequency} percentage representing the relative number of times
@@ -51,7 +51,7 @@ A linear model's importance \code{data.table} has the following columns:
\item \code{Class} (only for multiclass models) class label.
}
If \code{feature_names} is not provided and \code{model} doesn't have \code{feature_names},
If \code{feature_names} is not provided and \code{model} doesn't have \code{feature_names},
index of the features will be used instead. Because the index is extracted from the model dump
(based on C++ code), it starts at 0 (as in C/C++ or Python) instead of 1 (usual in R).
}
@@ -61,21 +61,21 @@ Creates a \code{data.table} of feature importances in a model.
\details{
This function works for both linear and tree models.
For linear models, the importance is the absolute magnitude of linear coefficients.
For that reason, in order to obtain a meaningful ranking by importance for a linear model,
the features need to be on the same scale (which you also would want to do when using either
For linear models, the importance is the absolute magnitude of linear coefficients.
For that reason, in order to obtain a meaningful ranking by importance for a linear model,
the features need to be on the same scale (which you also would want to do when using either
L1 or L2 regularization).
}
\examples{
# binomial classification using gbtree:
data(agaricus.train, package='xgboost')
bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2,
bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2,
eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
xgb.importance(model = bst)
# binomial classification using gblinear:
bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, booster = "gblinear",
bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, booster = "gblinear",
eta = 0.3, nthread = 1, nrounds = 20, objective = "binary:logistic")
xgb.importance(model = bst)