Description Usage Arguments Details Value Engine Details Note See Also Examples
boost_tree()
is a way to generate a specification of a model
before fitting and allows the model to be created using
different packages in R or via Spark. The main arguments for the
model are:
mtry
: The number of predictors that will be
randomly sampled at each split when creating the tree models.
trees
: The number of trees contained in the ensemble.
min_n
: The minimum number of data points in a node
that is required for the node to be split further.
tree_depth
: The maximum depth of the tree (i.e. number of
splits).
learn_rate
: The rate at which the boosting algorithm adapts
from iterationtoiteration.
loss_reduction
: The reduction in the loss function required
to split further.
sample_size
: The amount of data exposed to the fitting routine.
stop_iter
: The number of iterations without improvement before
stopping.
These arguments are converted to their specific names at the
time that the model is fit. Other options and arguments can be
set using the set_engine()
function. If left to their defaults
here (NULL
), the values are taken from the underlying model
functions. If parameters need to be modified, update()
can be used
in lieu of recreating the object from scratch.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27  boost_tree(
mode = "unknown",
mtry = NULL,
trees = NULL,
min_n = NULL,
tree_depth = NULL,
learn_rate = NULL,
loss_reduction = NULL,
sample_size = NULL,
stop_iter = NULL
)
## S3 method for class 'boost_tree'
update(
object,
parameters = NULL,
mtry = NULL,
trees = NULL,
min_n = NULL,
tree_depth = NULL,
learn_rate = NULL,
loss_reduction = NULL,
sample_size = NULL,
stop_iter = NULL,
fresh = FALSE,
...
)

mode 
A single character string for the type of model. Possible values for this model are "unknown", "regression", or "classification". 
mtry 
A number for the number (or proportion) of predictors that will
be randomly sampled at each split when creating the tree models ( 
trees 
An integer for the number of trees contained in the ensemble. 
min_n 
An integer for the minimum number of data points in a node that is required for the node to be split further. 
tree_depth 
An integer for the maximum depth of the tree (i.e. number
of splits) ( 
learn_rate 
A number for the rate at which the boosting algorithm adapts
from iterationtoiteration ( 
loss_reduction 
A number for the reduction in the loss function required
to split further ( 
sample_size 
A number for the number (or proportion) of data that is
exposed to the fitting routine. For 
stop_iter 
The number of iterations without improvement before
stopping ( 
object 
A boosted tree model specification. 
parameters 
A 1row tibble or named list with main
parameters to update. If the individual arguments are used,
these will supersede the values in 
fresh 
A logical for whether the arguments should be modified inplace of or replaced wholesale. 
... 
Not used for 
The data given to the function are not saved and are only used
to determine the mode of the model. For boost_tree()
, the
possible modes are "regression" and "classification".
The model can be created using the fit()
function using the
following engines:
R: "xgboost"
(the default), "C5.0"
Spark: "spark"
For this model, other packages may add additional engines. Use
show_engines()
to see the current set of engines.
An updated model specification.
Engines may have preset default arguments when executing the model fit call. For this type of model, the template of the fit calls are below:
boost_tree() %>% set_engine("xgboost") %>% set_mode("regression") %>% translate()
1 2 3 4 5 6 7  ## Boosted Tree Model Specification (regression)
##
## Computational engine: xgboost
##
## Model fit template:
## parsnip::xgb_train(x = missing_arg(), y = missing_arg(), nthread = 1,
## verbose = 0)

boost_tree() %>% set_engine("xgboost") %>% set_mode("classification") %>% translate()
1 2 3 4 5 6 7  ## Boosted Tree Model Specification (classification)
##
## Computational engine: xgboost
##
## Model fit template:
## parsnip::xgb_train(x = missing_arg(), y = missing_arg(), nthread = 1,
## verbose = 0)

Note that, for most engines to boost_tree()
, the sample_size
argument is in terms of the number of training set points. The
xgboost
package parameterizes this as the proportion of training set
samples instead. When using the tune
, this occurs automatically.
If you would like to use a custom range when tuning sample_size
, the
dials::sample_prop()
function can be used in that case. For example,
using a parameter set:
mod < boost_tree(sample_size = tune()) %>% set_engine("xgboost") %>% set_mode("classification") # update the parameters using the `dials` function mod_param < mod %>% parameters() %>% update(sample_size = sample_prop(c(0.4, 0.9)))
For this engine, tuning over trees
is very efficient since the same
model object can be used to make predictions over multiple values of
trees
.
Finally, note that xgboost
models require that nonnumeric predictors
(e.g., factors) must be converted to dummy variables or some other
numeric representation. By default, when using fit()
with xgboost
, a
onehot encoding is used to convert factor predictors to indicator
variables.
boost_tree() %>% set_engine("C5.0") %>% set_mode("classification") %>% translate()
1 2 3 4 5 6  ## Boosted Tree Model Specification (classification)
##
## Computational engine: C5.0
##
## Model fit template:
## parsnip::C5.0_train(x = missing_arg(), y = missing_arg(), weights = missing_arg())

Note that C50::C5.0()
does not require factor
predictors to be converted to indicator variables. fit()
does not
affect the encoding of the predictor values (i.e. factors stay factors)
for this model.
For this engine, tuning over trees
is very efficient since the same
model object can be used to make predictions over multiple values of
trees
.
boost_tree() %>% set_engine("spark") %>% set_mode("regression") %>% translate()
1 2 3 4 5 6 7  ## Boosted Tree Model Specification (regression)
##
## Computational engine: spark
##
## Model fit template:
## sparklyr::ml_gradient_boosted_trees(x = missing_arg(), formula = missing_arg(),
## type = "regression", seed = sample.int(10^5, 1))

boost_tree() %>% set_engine("spark") %>% set_mode("classification") %>% translate()
1 2 3 4 5 6 7  ## Boosted Tree Model Specification (classification)
##
## Computational engine: spark
##
## Model fit template:
## sparklyr::ml_gradient_boosted_trees(x = missing_arg(), formula = missing_arg(),
## type = "classification", seed = sample.int(10^5, 1))

fit()
does not affect the encoding of the predictor values
(i.e. factors stay factors) for this model.
The standardized parameter names in parsnip can be mapped to their original names in each engine that has main parameters. Each engine typically has a different default value (shown in parentheses) for each parameter.
parsnip  xgboost  C5.0  spark 
tree_depth  max_depth (6)  NA  max_depth (5) 
trees  nrounds (15)  trials (15)  max_iter (20) 
learn_rate  eta (0.3)  NA  step_size (0.1) 
mtry  colsample_bytree (1)  NA  feature_subset_strategy (see below) 
min_n  min_child_weight (1)  minCases (2)  min_instances_per_node (1) 
loss_reduction  gamma (0)  NA  min_info_gain (0) 
sample_size  subsample (1)  sample (0)  subsampling_rate (1) 
stop_iter  early_stop  NA  NA 
For spark, the default mtry
is the square root of the number of
predictors for classification, and onethird of the predictors for
regression.
For models created using the spark engine, there are
several differences to consider. First, only the formula
interface to via fit()
is available; using fit_xy()
will
generate an error. Second, the predictions will always be in a
spark table format. The names will be the same as documented but
without the dots. Third, there is no equivalent to factor
columns in spark tables so class predictions are returned as
character columns. Fourth, to retain the model object for a new
R session (via save()
), the model$fit
element of the parsnip
object should be serialized via ml_save(object$fit)
and
separately saved to disk. In a new session, the object can be
reloaded and reattached to the parsnip
object.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  show_engines("boost_tree")
boost_tree(mode = "classification", trees = 20)
# Parameters can be represented by a placeholder:
boost_tree(mode = "regression", mtry = varying())
model < boost_tree(mtry = 10, min_n = 3)
model
update(model, mtry = 1)
update(model, mtry = 1, fresh = TRUE)
param_values < tibble::tibble(mtry = 10, tree_depth = 5)
model %>% update(param_values)
model %>% update(param_values, mtry = 3)
param_values$verbose < 0
# Fails due to engine argument
# model %>% update(param_values)

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