xgboost::xgb.train() creates a series of decision trees forming an
ensemble. Each tree depends on the results of previous trees. All trees in
the ensemble are combined to produce a final prediction.
Details
For this engine, there are multiple modes: classification and regression
Tuning Parameters
This model has 8 tuning parameters:
tree_depth: Tree Depth (type: integer, default: 6L)trees: # Trees (type: integer, default: 15L)learn_rate: Learning Rate (type: double, default: 0.3)mtry: # Randomly Selected Predictors (type: integer, default: see below)min_n: Minimal Node Size (type: integer, default: 1L)loss_reduction: Minimum Loss Reduction (type: double, default: 0.0)sample_size: Proportion Observations Sampled (type: double, default: 1.0)stop_iter: # Iterations Before Stopping (type: integer, default: Inf)
For mtry, the default value of NULL translates to using all
available columns.
Translation from parsnip to the original package (regression)
boost_tree(
mtry = integer(), trees = integer(), min_n = integer(), tree_depth = integer(),
learn_rate = numeric(), loss_reduction = numeric(), sample_size = numeric(),
stop_iter = integer()
) %>%
set_engine("xgboost") %>%
set_mode("regression") %>%
translate()## Boosted Tree Model Specification (regression)
##
## Main Arguments:
## mtry = integer()
## trees = integer()
## min_n = integer()
## tree_depth = integer()
## learn_rate = numeric()
## loss_reduction = numeric()
## sample_size = numeric()
## stop_iter = integer()
##
## Computational engine: xgboost
##
## Model fit template:
## parsnip::xgb_train(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
## colsample_bynode = integer(), nrounds = integer(), min_child_weight = integer(),
## max_depth = integer(), eta = numeric(), gamma = numeric(),
## subsample = numeric(), early_stop = integer(), nthread = 1,
## verbose = 0)Translation from parsnip to the original package (classification)
boost_tree(
mtry = integer(), trees = integer(), min_n = integer(), tree_depth = integer(),
learn_rate = numeric(), loss_reduction = numeric(), sample_size = numeric(),
stop_iter = integer()
) %>%
set_engine("xgboost") %>%
set_mode("classification") %>%
translate()## Boosted Tree Model Specification (classification)
##
## Main Arguments:
## mtry = integer()
## trees = integer()
## min_n = integer()
## tree_depth = integer()
## learn_rate = numeric()
## loss_reduction = numeric()
## sample_size = numeric()
## stop_iter = integer()
##
## Computational engine: xgboost
##
## Model fit template:
## parsnip::xgb_train(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
## colsample_bynode = integer(), nrounds = integer(), min_child_weight = integer(),
## max_depth = integer(), eta = numeric(), gamma = numeric(),
## subsample = numeric(), early_stop = integer(), nthread = 1,
## verbose = 0)xgb_train() is a wrapper around
xgboost::xgb.train() (and other functions)
that makes it easier to run this model.
Preprocessing requirements
xgboost does not have a means to translate factor predictors to grouped
splits. Factor/categorical predictors need to be converted to numeric
values (e.g., dummy or indicator variables) for this engine. When using
the formula method via fit.model_spec(), parsnip
will convert factor columns to indicators using a one-hot encoding.
For classification, non-numeric outcomes (i.e., factors) are internally
converted to numeric. For binary classification, the event_level
argument of set_engine() can be set to either "first" or "second"
to specify which level should be used as the event. This can be helpful
when a watchlist is used to monitor performance from with the xgboost
training process.
Case weights
This model can utilize case weights during model fitting. To use them,
see the documentation in case_weights and the examples
on tidymodels.org.
The fit() and fit_xy() arguments have arguments called
case_weights that expect vectors of case weights.
Sparse Data
This model can utilize sparse data during model fitting and prediction.
Both sparse matrices such as dgCMatrix from the Matrix package and
sparse tibbles from the sparsevctrs package are supported. See
sparse_data for more information.
Other details
Interfacing with the params argument
The xgboost function that parsnip indirectly wraps,
xgboost::xgb.train(), takes most arguments via
the params list argument. To supply engine-specific arguments that are
documented in xgboost::xgb.train() as
arguments to be passed via params, supply the list elements directly
as named arguments to set_engine() rather than as
elements in params. For example, pass a non-default evaluation metric
like this:
# good
boost_tree() %>%
set_engine("xgboost", eval_metric = "mae")## Boosted Tree Model Specification (unknown mode)
##
## Engine-Specific Arguments:
## eval_metric = mae
##
## Computational engine: xgboost…rather than this:
# bad
boost_tree() %>%
set_engine("xgboost", params = list(eval_metric = "mae"))## Boosted Tree Model Specification (unknown mode)
##
## Engine-Specific Arguments:
## params = list(eval_metric = "mae")
##
## Computational engine: xgboostparsnip will then route arguments as needed. In the case that arguments
are passed to params via set_engine(), parsnip will
warn and re-route the arguments as needed. Note, though, that arguments
passed to params cannot be tuned.
Sparse matrices
xgboost requires the data to be in a sparse format. If your predictor
data are already in this format, then use
fit_xy.model_spec() to pass it to the model
function. Otherwise, parsnip converts the data to this format.
Parallel processing
By default, the model is trained without parallel processing. This can
be change by passing the nthread parameter to
set_engine(). However, it is unwise to combine this
with external parallel processing when using the package.
Interpreting mtry
The mtry argument denotes the number of predictors that will be
randomly sampled at each split when creating tree models.
Some engines, such as "xgboost", "xrf", and "lightgbm", interpret
their analogue to the mtry argument as the proportion of predictors
that will be randomly sampled at each split rather than the count. In
some settings, such as when tuning over preprocessors that influence the
number of predictors, this parameterization is quite
helpful—interpreting mtry as a proportion means that [0, 1] is
always a valid range for that parameter, regardless of input data.
parsnip and its extensions accommodate this parameterization using the
counts argument: a logical indicating whether mtry should be
interpreted as the number of predictors that will be randomly sampled at
each split. TRUE indicates that mtry will be interpreted in its
sense as a count, FALSE indicates that the argument will be
interpreted in its sense as a proportion.
mtry is a main model argument for
boost_tree() and
rand_forest(), and thus should not have an
engine-specific interface. So, regardless of engine, counts defaults
to TRUE. For engines that support the proportion interpretation
(currently "xgboost" and "xrf", via the rules package, and
"lightgbm" via the bonsai package) the user can pass the
counts = FALSE argument to set_engine() to supply mtry values
within [0, 1].
Early stopping
The stop_iter() argument allows the model to prematurely stop training
if the objective function does not improve within early_stop
iterations.
The best way to use this feature is in conjunction with an internal
validation set. To do this, pass the validation parameter of
xgb_train() via the parsnip
set_engine() function. This is the
proportion of the training set that should be reserved for measuring
performance (and stopping early).
If the model specification has early_stop >= trees, early_stop is
converted to trees - 1 and a warning is issued.
Note that, since the validation argument provides an alternative
interface to watchlist, the watchlist argument is guarded by parsnip
and will be ignored (with a warning) if passed.
Objective function
parsnip chooses the objective function based on the characteristics of
the outcome. To use a different loss, pass the objective argument to
set_engine() directly.
Saving fitted model objects
This model object contains data that are not required to make predictions. When saving the model for the purpose of prediction, the size of the saved object might be substantially reduced by using functions from the butcher package.
Models fitted with this engine may require native serialization methods to be properly saved and/or passed between R sessions. To learn more about preparing fitted models for serialization, see the bundle package.
Examples
The “Fitting and Predicting with parsnip” article contains
examples
for boost_tree() with the "xgboost" engine.