details_boost_tree_h2o: Boosted trees via h2o

In parsnip: A Common API to Modeling and Analysis Functions

Boosted trees via h2o

Description

h2o::h2o.xgboost() 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:

• trees: # Trees (type: integer, default: 50)

• tree_depth: Tree Depth (type: integer, default: 6)

• min_n: Minimal Node Size (type: integer, default: 1)

• learn_rate: Learning Rate (type: double, default: 0.3)

• sample_size: # Observations Sampled (type: integer, default: 1)

• mtry: # Randomly Selected Predictors (type: integer, default: 1)

• loss_reduction: Minimum Loss Reduction (type: double, default: 0)

• stop_iter: # Iterations Before Stopping (type: integer, default: 0)

min_n represents the fewest allowed observations in a terminal node, h2o::h2o.xgboost() allows only one row in a leaf by default.

stop_iter controls early stopping rounds based on the convergence of the engine parameter stopping_metric. By default, h2o::h2o.xgboost() does not use early stopping. When stop_iter is not 0, h2o::h2o.xgboost() uses logloss for classification, deviance for regression and anonomaly score for Isolation Forest. This is mostly useful when used alongside the engine parameter validation, which is the proportion of train-validation split, parsnip will split and pass the two data frames to h2o. Then h2o::h2o.xgboost() will evaluate the metric and early stopping criteria on the validation set.

Translation from parsnip to the original package (regression)

agua::h2o_train_xgboost() is a wrapper around h2o::h2o.xgboost().

The agua extension package is required to fit this model.

boost_tree(
  mtry = integer(), trees = integer(), tree_depth = integer(), 
  learn_rate = numeric(), min_n = integer(), loss_reduction = numeric(), stop_iter = integer()
) %>%
  set_engine("h2o") %>%
  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()
##   stop_iter = integer()
## 
## Computational engine: h2o 
## 
## Model fit template:
## agua::h2o_train_xgboost(x = missing_arg(), y = missing_arg(), 
##     weights = missing_arg(), validation_frame = missing_arg(), 
##     col_sample_rate = integer(), ntrees = integer(), min_rows = integer(), 
##     max_depth = integer(), learn_rate = numeric(), min_split_improvement = numeric(), 
##     stopping_rounds = integer())

Translation from parsnip to the original package (classification)

The agua extension package is required to fit this model.

boost_tree(
  mtry = integer(), trees = integer(), tree_depth = integer(), 
  learn_rate = numeric(), min_n = integer(), loss_reduction = numeric(), stop_iter = integer()
) %>% 
  set_engine("h2o") %>% 
  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()
##   stop_iter = integer()
## 
## Computational engine: h2o 
## 
## Model fit template:
## agua::h2o_train_xgboost(x = missing_arg(), y = missing_arg(), 
##     weights = missing_arg(), validation_frame = missing_arg(), 
##     col_sample_rate = integer(), ntrees = integer(), min_rows = integer(), 
##     max_depth = integer(), learn_rate = numeric(), min_split_improvement = numeric(), 
##     stopping_rounds = integer())

Preprocessing

This engine does not require any special encoding of the predictors. Categorical predictors can be partitioned into groups of factor levels (e.g. ⁠{a, c}⁠ vs ⁠{b, d}⁠) when splitting at a node. Dummy variables are not required for this model.

Non-numeric predictors (i.e., factors) are internally converted to numeric. In the classification context, non-numeric outcomes (i.e., factors) are also internally converted to numeric.

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]⁠.

Initializing h2o

To use the h2o engine with tidymodels, please run h2o::h2o.init() first. By default, This connects R to the local h2o server. This needs to be done in every new R session. You can also connect to a remote h2o server with an IP address, for more details see h2o::h2o.init().

You can control the number of threads in the thread pool used by h2o with the nthreads argument. By default, it uses all CPUs on the host. This is different from the usual parallel processing mechanism in tidymodels for tuning, while tidymodels parallelizes over resamples, h2o parallelizes over hyperparameter combinations for a given resample.

h2o will automatically shut down the local h2o instance started by R when R is terminated. To manually stop the h2o server, run h2o::h2o.shutdown().

Saving fitted model objects

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.

parsnip documentation built on June 24, 2024, 5:14 p.m.