pipe_xgboost: eXtrem Gradient Boosted models
In jmaspons/MLTools: Pipes and tools for machine learning

View source: R/pipe_xgboost.R

pipe_xgboost

R Documentation

eXtrem Gradient Boosted models

Description

eXtrem Gradient Boosted models

Usage

pipe_xgboost(
  df,
  predInput = NULL,
  responseVars = 1,
  caseClass = NULL,
  idVars = character(),
  weight = "class",
  crossValStrategy = c("Kfold", "bootstrap"),
  k = 5,
  replicates = 10,
  crossValRatio = c(train = 0.6, test = 0.2, validate = 0.2),
  params = list(),
  nrounds = 5,
  shap = TRUE,
  aggregate_shap = TRUE,
  repVi = 5,
  summarizePred = TRUE,
  scaleDataset = FALSE,
  XGBmodel = FALSE,
  DALEXexplainer = FALSE,
  variableResponse = FALSE,
  save_validateset = FALSE,
  baseFilenameXDG = NULL,
  filenameRasterPred = NULL,
  tempdirRaster = NULL,
  nCoresRaster = parallel::detectCores()%/%2,
  verbose = 0,
  ...
)

Arguments

`df`	a `data.frame` with the data.
`predInput`	a `data.frame` or a `Raster` with the input variables for the model as columns or layers. The columns or layer names must match the names of `df` columns.
`responseVars`	response variables as column names or indexes on `df`.
`caseClass`	class of the samples used to weight cases. Column names or indexes on `df`, or a vector with the class for each rows in `df`.
`idVars`	id column names or indexes on `df`. This columns will not be used for training.
`weight`	Optional array of the same length as `nrow(df)`, containing weights to apply to the model's loss for each sample.
`crossValStrategy`	`Kfold` or `bootstrap`.
`k`	number of data partitions when `crossValStrategy="Kfold"`.
`replicates`	number of replicates for `crossValStrategy="bootstrap"` and `crossValStrategy="Kfold"` (`replicates * k-1`, 1 fold for validation).
`crossValRatio`	proportion of the dataset used to train, test and validate the model when `crossValStrategy="bootstrap"`. Default to `c(train=0.6, test=0.2, validate=0.2)`. If there is only one value, will be taken as a train proportion and the test set will be used for validation.
`params`	the list of parameters to `xgboost::xgb.train()`. The complete list of parameters is available in the online documentation.
`nrounds`	max number of boosting iterations.
`shap`	if `TRUE`, return the SHAP values as `shapviz::shapviz()` objects.
`aggregate_shap`	if `TRUE`, and `shap` is also `TRUE`, aggregate SHAP from all replicates.
`repVi`	replicates of the permutations to calculate the importance of the variables. 0 to avoid calculating variable importance.
`summarizePred`	if `TRUE`, return the mean, sd and se of the predictors. if `FALSE`, return the predictions for each replicate.
`scaleDataset`	if `TRUE`, scale the whole dataset only once instead of the train set at each replicate. Optimize processing time for predictions with large rasters.
`XGBmodel`	if `TRUE`, return the model with the result.
`DALEXexplainer`	if `TRUE`, return a explainer for the models from `DALEX::explain()` function. It doesn't work with multisession future plans.
`variableResponse`	if `TRUE`, return aggregated_profiles_explainer object from `ingredients::partial_dependency()` and the coefficients of the adjusted linear model.
`save_validateset`	save the validateset (independent data not used for training).
`baseFilenameXDG`	if no missing, save the NN in hdf5 format on this path with iteration appended.
`filenameRasterPred`	if no missing, save the predictions in a RasterBrick to this file.
`tempdirRaster`	path to a directory to save temporal raster files.
`nCoresRaster`	number of cores used for parallelized raster cores. Use half of the available cores by default.
`verbose`	if > 0, print the state. The bigger the more information printed.
`...`	extra parameters for `xgboost::xgb.train()`, `future.apply::future_replicate()` or `ingredients::feature_importance()`.