Predict method for the gbt function

Predict method for the gbt function

# S3 method for gbt
predict(
  object,
  pred_data = NULL,
  pred_cmd = "",
  dec = 3,
  envir = parent.frame(),
  ...
)

Arguments

object	Return value from gbt
pred_data	Provide the dataframe to generate predictions (e.g., diamonds). The dataset must contain all columns used in the estimation
pred_cmd	Generate predictions using a command. For example, `pclass = levels(pclass)` would produce predictions for the different levels of factor `pclass`. To add another variable, create a vector of prediction strings, (e.g., c('pclass = levels(pclass)', 'age = seq(0,100,20)')
dec	Number of decimals to show
envir	Environment to extract data from
...	further arguments passed to or from other methods

Details

See https://radiant-rstats.github.io/docs/model/gbt.html for an example in Radiant

See also

gbt to generate the result

summary.gbt to summarize results

Examples

result <- gbt(titanic, "survived", c("pclass", "sex"), early_stopping_rounds = 0)
predict(result, pred_cmd = "pclass = levels(pclass)")
#> Gradiant Boosted Trees
#> Data                 : titanic 
#> Response variable    : survived 
#> Level(s)             : Yes in survived 
#> Explanatory variables: pclass, sex 
#> Prediction command   : pclass = levels(pclass) 
#> 
#>   sex pclass Prediction
#>  male    1st      0.457
#>  male    2nd      0.398
#>  male    3rd      0.403
result <- gbt(diamonds, "price", "carat:color", type = "regression")
predict(result, pred_cmd = "carat = 1:3")
#> Gradiant Boosted Trees
#> Data                 : diamonds 
#> Response variable    : price 
#> Explanatory variables: carat, clarity, cut, color 
#> Prediction command   : carat = 1:3 
#> 
#>  clarity   cut color carat Prediction
#>      SI1 Ideal     G     1   4855.246
#>      SI1 Ideal     G     2  16499.922
#>      SI1 Ideal     G     3  19909.562
predict(result, pred_data = diamonds) %>% head()
#> Gradiant Boosted Trees
#> Data                 : diamonds 
#> Response variable    : price 
#> Explanatory variables: carat, clarity, cut, color 
#> Prediction dataset   : diamonds 
#> 
#>  carat clarity       cut color Prediction
#>  0.320     VS1     Ideal     H    579.664
#>  0.340     SI1 Very Good     G    546.916
#>  0.300     VS2 Very Good     G    566.852
#>  0.350    VVS2     Ideal     H    720.258
#>  0.400     VS2   Premium     F    909.794
#>  0.600    VVS1     Ideal     E   3264.439