lgbm.fi: LightGBM Feature Importance
In Laurae2/Laurae: Advanced High Performance Data Science Toolbox for R

Description Usage Arguments Value Examples

This function allows to get the feature importance on a LightGBM model. The model file must be "workingdir", where "workingdir" is the folder and input_model is the model file name.

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lgbm.fi(model, workingdir = ifelse(is.list(model), model[["Path"]], getwd()),
  input_model = ifelse(is.list(model), model[["Name"]], "lgbm_model.txt"),
  feature_names = NA, ntreelimit = 0, data.table = TRUE)

`model`	Type: list. The model file. If a character vector is provided, it is considered to be the model which is going to be saved as `input_model`. If a list is provided, it is used to setup to fetch the correct variables, which you can override by setting the arguments manually. If a single value is provided (like `NA`), then it is ignored and uses the other arguments to fetch the model locally.
`workingdir`	Type: character. The working directory of the model file. Defaults to `ifelse(is.list(model), model[["Path"]], getwd())`, which means "take the model working directory if provided the model list, else take the default working directory".
`input_model`	Type: character. The file name of the model. Defaults to `ifelse(is.list(model), model[["Name"]], 'lgbm_model.txt')`, which means "take the input model name if provided the model list, else take "lgbm_model.txt".
`feature_names`	Type: vector of characters. The names of the features, in the order they were fed to LightGBM. Returns column numbers if left as `NA`. Defaults to `NA`.
`ntreelimit`	Type: integer. The number of trees to select, starting from the first tree. Defaults to `0`.
`data.table`	Type: boolean. Whether to return a data.table (`TRUE`) or a data.frame (`FALSE`). Defaults to `TRUE`.

A data.table (or data.frame) with 9 columns: c("Feature", "Gain", "Gain_Rel_Ratio", "Gain_Abs_Ratio", "Gain_Std", "Gain_Std_Rel_Ratio", "Gain_Std_Abs_Ratio", "Freq", "Freq_Rel_Ratio", "Freq_Abs_Ratio")

## Not run: 
# Feature importance on a single model without any tree limit.
lgbm.fi(model = trained, feature_names = colnames(data), ntreelimit = 0)

# Feature importance on the first model from a cross-validation without any tree limit.
lgbm.fi(model = trained.cv[["Models"]][[1]], feature_names = colnames(data))

## End(Not run)