s_XGBoost: XGBoost Classification and Regression (C, R)

In egenn/rtemis: Machine Learning and Visualization

XGBoost Classification and Regression (C, R)

Description

Tune hyperparameters using grid search and resampling, train a final model, and validate it

Usage

s_XGBoost(
  x,
  y = NULL,
  x.test = NULL,
  y.test = NULL,
  x.name = NULL,
  y.name = NULL,
  booster = c("gbtree", "gblinear", "dart"),
  missing = NA,
  nrounds = 1000L,
  force.nrounds = NULL,
  weights = NULL,
  ifw = TRUE,
  ifw.type = 2,
  upsample = FALSE,
  downsample = FALSE,
  resample.seed = NULL,
  obj = NULL,
  feval = NULL,
  xgb.verbose = NULL,
  print_every_n = 100L,
  early_stopping_rounds = 50L,
  eta = 0.01,
  gamma = 0,
  max_depth = 2,
  min_child_weight = 5,
  max_delta_step = 0,
  subsample = 0.75,
  colsample_bytree = 1,
  colsample_bylevel = 1,
  lambda = 0,
  alpha = 0,
  tree_method = "auto",
  sketch_eps = 0.03,
  num_parallel_tree = 1,
  base_score = NULL,
  objective = NULL,
  sample_type = "uniform",
  normalize_type = "forest",
  rate_drop = 0,
  one_drop = 0,
  skip_drop = 0,
  grid.resample.params = setup.resample("kfold", 5),
  gridsearch.type = "exhaustive",
  metric = NULL,
  maximize = NULL,
  importance = NULL,
  print.plot = FALSE,
  plot.fitted = NULL,
  plot.predicted = NULL,
  plot.theme = rtTheme,
  question = NULL,
  verbose = TRUE,
  grid.verbose = FALSE,
  trace = 0,
  save.gridrun = FALSE,
  n.cores = 1,
  nthread = rtCores,
  outdir = NULL,
  save.mod = ifelse(!is.null(outdir), TRUE, FALSE),
  .gs = FALSE,
  ...
)

Arguments

x	Numeric vector or matrix / data frame of features i.e. independent variables
y	Numeric vector of outcome, i.e. dependent variable
x.test	Numeric vector or matrix / data frame of testing set features Columns must correspond to columns in x
y.test	Numeric vector of testing set outcome
x.name	Character: Name for feature set
y.name	Character: Name for outcome
booster	Character: "gbtree", "gblinear": Booster to use.
missing	String or Numeric: Which values to consider as missing.
nrounds	Integer: Maximum number of rounds to run. Can be set to a high number as early stopping will limit nrounds by monitoring inner CV error
force.nrounds	Integer: Number of rounds to run if not estimating optimal number by CV
weights	Numeric vector: Weights for cases. For classification, weights takes precedence over ifw, therefore set weights = NULL if using ifw. Note: If weight are provided, ifw is not used. Leave NULL if setting ifw = TRUE.
ifw	Logical: If TRUE, apply inverse frequency weighting (for Classification only). Note: If weights are provided, ifw is not used.
ifw.type	Integer 0, 1, 2 1: class.weights as in 0, divided by min(class.weights) 2: class.weights as in 0, divided by max(class.weights)
upsample	Logical: If TRUE, upsample cases to balance outcome classes (for Classification only) Note: upsample will randomly sample with replacement if the length of the majority class is more than double the length of the class you are upsampling, thereby introducing randomness
downsample	Logical: If TRUE, downsample majority class to match size of minority class
resample.seed	Integer: If provided, will be used to set the seed during upsampling. Default = NULL (random seed)
obj	Function: Custom objective function. See ?xgboost::xgboost
feval	Function: Custom evaluation function. See ?xgboost::xgboost
xgb.verbose	Integer: Verbose level for XGB learners used for tuning.
print_every_n	Integer: Print evaluation metrics every this many iterations
early_stopping_rounds	Integer: Training on resamples of x.train (tuning) will stop if performance does not improve for this many rounds
eta	[gS] Numeric (0, 1): Learning rate.
gamma	[gS] Numeric: Minimum loss reduction required to make further partition
max_depth	[gS] Integer: Maximum tree depth.
min_child_weight	[gS] Numeric: Minimum sum of instance weight needed in a child.
max_delta_step	[gS] Numeric: Maximum delta step we allow each leaf output to be. O means no constraint. 1-10 may help control the update, especially with imbalanced outcomes.
subsample	[gS] Numeric: subsample ratio of the training instance
colsample_bytree	[gS] Numeric: subsample ratio of columns when constructing each tree
colsample_bylevel	[gS] Numeric
lambda	[gS] L2 regularization on weights
alpha	[gS] L1 regularization on weights
tree_method	[gS] XGBoost tree construction algorithm
sketch_eps	[gS] Numeric (0, 1):
num_parallel_tree	Integer: N of trees to grow in parallel: Results in Random Forest -like algorithm. (Default = 1; i.e. regular boosting)
base_score	Numeric: The mean outcome response.
objective	(Default = NULL)
sample_type	Character: Type of sampling algorithm for dart booster "uniform": dropped trees are selected uniformly. "weighted": dropped trees are selected in proportion to weight.
normalize_type	Character.
rate_drop	[gS] Numeric: Dropout rate for dart booster.
one_drop	[gS] Integer 0, 1: When this flag is enabled, at least one tree is always dropped during the dropout.
skip_drop	[gS] Numeric [0, 1]: Probability of skipping the dropout procedure during a boosting iteration. If a dropout is skipped, new trees are added in the same manner as gbtree. Non-zero skip_drop has higher priority than rate_drop or one_drop.
grid.resample.params	List: Output of setup.resample defining grid search parameters.
gridsearch.type	Character: Type of grid search to perform: "exhaustive" or "randomized".
metric	Character: Metric to minimize, or maximize if maximize = TRUE during grid search. Default = NULL, which results in "Balanced Accuracy" for Classification, "MSE" for Regression, and "Coherence" for Survival Analysis.
maximize	Logical: If TRUE, metric will be maximized if grid search is run.
importance	Logical: If TRUE, calculate variable importance.
print.plot	Logical: if TRUE, produce plot using mplot3 Takes precedence over plot.fitted and plot.predicted.
plot.fitted	Logical: if TRUE, plot True (y) vs Fitted
plot.predicted	Logical: if TRUE, plot True (y.test) vs Predicted. Requires x.test and y.test
plot.theme	Character: "zero", "dark", "box", "darkbox"
question	Character: the question you are attempting to answer with this model, in plain language.
verbose	Logical: If TRUE, print summary to screen.
grid.verbose	Logical: Passed to gridSearchLearn
trace	Integer: If > 0, print parameter values to console.
save.gridrun	Logical: If TRUE, save grid search models.
n.cores	Integer: Number of cores to use.
nthread	Integer: Number of threads for xgboost using OpenMP. Only parallelize resamples using n.cores or the xgboost execution using this setting. At the moment of writing, parallelization via this parameter causes a linear booster to fail most of the times. Therefore, default is rtCores for 'gbtree', 1 for 'gblinear'
outdir	Path to output directory. If defined, will save Predicted vs. True plot, if available, as well as full model output, if save.mod is TRUE
save.mod	Logical: If TRUE, save all output to an RDS file in outdir save.mod is TRUE by default if an outdir is defined. If set to TRUE, and no outdir is defined, outdir defaults to paste0("./s.", mod.name)
.gs	Internal use only
...	Additional arguments passed to xgboost::xgb.train

Details

[gS]: indicates parameter will be autotuned by grid search if multiple values are passed. Learn more about XGBoost's parameters here: http://xgboost.readthedocs.io/en/latest/parameter.html

Value

rtMod object

Author(s)

E.D. Gennatas

See Also

train_cv for external cross-validation

Other Supervised Learning: s_AdaBoost(), s_AddTree(), s_BART(), s_BRUTO(), s_BayesGLM(), s_C50(), s_CART(), s_CTree(), s_EVTree(), s_GAM(), s_GAM.default(), s_GAM.formula(), s_GBM(), s_GLM(), s_GLMNET(), s_GLMTree(), s_GLS(), s_H2ODL(), s_H2OGBM(), s_H2ORF(), s_HAL(), s_KNN(), s_LDA(), s_LM(), s_LMTree(), s_LightCART(), s_LightGBM(), s_MARS(), s_MLRF(), s_NBayes(), s_NLA(), s_NLS(), s_NW(), s_PPR(), s_PolyMARS(), s_QDA(), s_QRNN(), s_RF(), s_RFSRC(), s_Ranger(), s_SDA(), s_SGD(), s_SPLS(), s_SVM(), s_TFN(), s_XRF()

Other Tree-based methods: s_AdaBoost(), s_AddTree(), s_BART(), s_C50(), s_CART(), s_CTree(), s_EVTree(), s_GBM(), s_GLMTree(), s_H2OGBM(), s_H2ORF(), s_LMTree(), s_LightCART(), s_LightGBM(), s_MLRF(), s_RF(), s_RFSRC(), s_Ranger(), s_XRF()

egenn/rtemis documentation built on May 4, 2024, 7:40 p.m.