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modeltime

xgboost_impl

Wrapper for parsnip::xgb_train

Description

Wrapper for parsnip::xgb_train

Usage

xgboost_impl(
  x,
  y,
  max_depth = 6,
  nrounds = 15,
  eta = 0.3,
  colsample_bytree = 1,
  min_child_weight = 1,
  gamma = 0,
  subsample = 1,
  validation = 0,
  early_stop = NULL,
  ...
)

Arguments

`x`	A data frame or matrix of predictors
`y`	A vector (factor or numeric) or matrix (numeric) of outcome data.
`max_depth`	An integer for the maximum depth of the tree.
`nrounds`	An integer for the number of boosting iterations.
`eta`	A numeric value between zero and one to control the learning rate.
`colsample_bytree`	Subsampling proportion of columns.
`min_child_weight`	A numeric value for the minimum sum of instance weights needed in a child to continue to split.
`gamma`	A number for the minimum loss reduction required to make a further partition on a leaf node of the tree
`subsample`	Subsampling proportion of rows.
`validation`	A positive number. If on `[0, 1)` the value, `validation` is a random proportion of data in `x` and `y` that are used for performance assessment and potential early stopping. If 1 or greater, it is the number of training set samples use for these purposes.
`early_stop`	An integer or `NULL`. If not `NULL`, it is the number of training iterations without improvement before stopping. If `validation` is used, performance is base on the validation set; otherwise the training set is used.
`...`	Other options to pass to `xgb.train`.

modeltime

The Tidymodels Extension for Time Series Modeling

v0.5.1

MIT + file LICENSE

Authors

Matt Dancho [aut, cre], Business Science [cph]

Initial release

xgboost_impl

Description

Usage

Arguments

modeltime

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