details_rule_fit_xrf | R Documentation |
xrf::xrf()
fits a model that derives simple feature rules from a tree
ensemble and uses the rules as features to a regularized model. rules::xrf_fit()
is a wrapper around this function.
For this engine, there are multiple modes: classification and regression
This model has 8 tuning parameters:
mtry
: Proportion Randomly Selected Predictors (type: double,
default: see below)
trees
: # Trees (type: integer, default: 15L)
min_n
: Minimal Node Size (type: integer, default: 1L)
tree_depth
: Tree Depth (type: integer, default: 6L)
learn_rate
: Learning Rate (type: double, default: 0.3)
loss_reduction
: Minimum Loss Reduction (type: double, default: 0.0)
sample_size
: Proportion Observations Sampled (type: double, default:
1.0)
penalty
: Amount of Regularization (type: double, default: 0.1)
The rules extension package is required to fit this model.
library(rules) rule_fit( mtry = numeric(1), trees = integer(1), min_n = integer(1), tree_depth = integer(1), learn_rate = numeric(1), loss_reduction = numeric(1), sample_size = numeric(1), penalty = numeric(1) ) %>% set_engine("xrf") %>% set_mode("regression") %>% translate()
## RuleFit Model Specification (regression) ## ## Main Arguments: ## mtry = numeric(1) ## trees = integer(1) ## min_n = integer(1) ## tree_depth = integer(1) ## learn_rate = numeric(1) ## loss_reduction = numeric(1) ## sample_size = numeric(1) ## penalty = numeric(1) ## ## Computational engine: xrf ## ## Model fit template: ## rules::xrf_fit(formula = missing_arg(), data = missing_arg(), ## xgb_control = missing_arg(), colsample_bynode = numeric(1), ## nrounds = integer(1), min_child_weight = integer(1), max_depth = integer(1), ## eta = numeric(1), gamma = numeric(1), subsample = numeric(1), ## lambda = numeric(1))
The rules extension package is required to fit this model.
library(rules) rule_fit( mtry = numeric(1), trees = integer(1), min_n = integer(1), tree_depth = integer(1), learn_rate = numeric(1), loss_reduction = numeric(1), sample_size = numeric(1), penalty = numeric(1) ) %>% set_engine("xrf") %>% set_mode("classification") %>% translate()
## RuleFit Model Specification (classification) ## ## Main Arguments: ## mtry = numeric(1) ## trees = integer(1) ## min_n = integer(1) ## tree_depth = integer(1) ## learn_rate = numeric(1) ## loss_reduction = numeric(1) ## sample_size = numeric(1) ## penalty = numeric(1) ## ## Computational engine: xrf ## ## Model fit template: ## rules::xrf_fit(formula = missing_arg(), data = missing_arg(), ## xgb_control = missing_arg(), colsample_bynode = numeric(1), ## nrounds = integer(1), min_child_weight = integer(1), max_depth = integer(1), ## eta = numeric(1), gamma = numeric(1), subsample = numeric(1), ## lambda = numeric(1))
Note that, per the documentation in ?xrf
, transformations of the
response variable are not supported. To use these with rule_fit()
, we
recommend using a recipe instead of the formula method.
Also, there are several configuration differences in how xrf()
is fit
between that package and the wrapper used in rules. Some differences
in default values are:
parameter | xrf | rules |
trees | 100 | 15 |
max_depth | 3 | 6 |
These differences will create a disparity in the values of the penalty
argument that glmnet uses. Also, rules can also set penalty
whereas xrf uses an internal 5-fold cross-validation to determine it
(by default).
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()
, parsnip will
convert factor columns to indicators.
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]
.
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.
The underlying model implementation does not allow for case weights.
Friedman and Popescu. “Predictive learning via rule ensembles.” Ann. Appl. Stat. 2 (3) 916- 954, September 2008
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