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[R] Re-generate Roxygen2 doc (#6915)

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This commit is contained in:
Philip Hyunsu Cho
2021-04-29 11:55:07 -07:00
committed by GitHub
parent 20f34d9776
commit 5472ef626c
10 changed files with 23 additions and 28 deletions
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17
R-package/man/cb.gblinear.history.Rd
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@@ -8,7 +8,7 @@ during its training.}
cb.gblinear.history(sparse = FALSE)
}
\arguments{
\item{sparse}{when set to FALSE/TURE, a dense/sparse matrix is used to store the result.
\item{sparse}{when set to FALSE/TRUE, a dense/sparse matrix is used to store the result.
Sparse format is useful when one expects only a subset of coefficients to be non-zero,
when using the "thrifty" feature selector with fairly small number of top features
selected per iteration.}
@@ -36,7 +36,6 @@ Callback function expects the following values to be set in its calling frame:
#
# In the iris dataset, it is hard to linearly separate Versicolor class from the rest
# without considering the 2nd order interactions:
require(magrittr)
x <- model.matrix(Species ~ .^2, iris)[,-1]
colnames(x)
dtrain <- xgb.DMatrix(scale(x), label = 1*(iris$Species == "versicolor"))
@@ -57,7 +56,7 @@ matplot(coef_path, type = 'l')
bst <- xgb.train(param, dtrain, list(tr=dtrain), nrounds = 200, eta = 0.8,
updater = 'coord_descent', feature_selector = 'thrifty', top_k = 1,
callbacks = list(cb.gblinear.history()))
xgb.gblinear.history(bst) \%>\% matplot(type = 'l')
matplot(xgb.gblinear.history(bst), type = 'l')
# Componentwise boosting is known to have similar effect to Lasso regularization.
# Try experimenting with various values of top_k, eta, nrounds,
# as well as different feature_selectors.
@@ -66,7 +65,7 @@ xgb.gblinear.history(bst) \%>\% matplot(type = 'l')
bst <- xgb.cv(param, dtrain, nfold = 5, nrounds = 100, eta = 0.8,
callbacks = list(cb.gblinear.history()))
# coefficients in the CV fold #3
xgb.gblinear.history(bst)[[3]] \%>\% matplot(type = 'l')
matplot(xgb.gblinear.history(bst)[[3]], type = 'l')
#### Multiclass classification:
@@ -79,15 +78,15 @@ param <- list(booster = "gblinear", objective = "multi:softprob", num_class = 3,
bst <- xgb.train(param, dtrain, list(tr=dtrain), nrounds = 70, eta = 0.5,
callbacks = list(cb.gblinear.history()))
# Will plot the coefficient paths separately for each class:
xgb.gblinear.history(bst, class_index = 0) \%>\% matplot(type = 'l')
xgb.gblinear.history(bst, class_index = 1) \%>\% matplot(type = 'l')
xgb.gblinear.history(bst, class_index = 2) \%>\% matplot(type = 'l')
matplot(xgb.gblinear.history(bst, class_index = 0), type = 'l')
matplot(xgb.gblinear.history(bst, class_index = 1), type = 'l')
matplot(xgb.gblinear.history(bst, class_index = 2), type = 'l')
# CV:
bst <- xgb.cv(param, dtrain, nfold = 5, nrounds = 70, eta = 0.5,
callbacks = list(cb.gblinear.history(FALSE)))
# 1st forld of 1st class
xgb.gblinear.history(bst, class_index = 0)[[1]] \%>\% matplot(type = 'l')
# 1st fold of 1st class
matplot(xgb.gblinear.history(bst, class_index = 0)[[1]], type = 'l')
}
\seealso{