Running prediction on data frame of remote database table, without needing to load original packages used to fit model.
Usage
# S3 method for class 'orbital_class'
predict(object, new_data, ...)Arguments
- object
An orbital object.
- new_data
A data frame or remote database table.
- ...
Not currently used.
Details
Using this function should give identical results to running predict() or
bake() on the orginal object.
The prediction done will only return prediction colunms, a opposed to
returning all modified functions as done with orbital_inline().
Examples
library(workflows)
library(recipes)
library(parsnip)
rec_spec <- recipe(mpg ~ ., data = mtcars) %>%
step_normalize(all_numeric_predictors())
lm_spec <- linear_reg()
wf_spec <- workflow(rec_spec, lm_spec)
wf_fit <- fit(wf_spec, mtcars)
orbital_obj <- orbital(wf_fit)
predict(orbital_obj, mtcars)
#> .pred
#> Mazda RX4 22.59951
#> Mazda RX4 Wag 22.11189
#> Datsun 710 26.25064
#> Hornet 4 Drive 21.23740
#> Hornet Sportabout 17.69343
#> Valiant 20.38304
#> Duster 360 14.38626
#> Merc 240D 22.49601
#> Merc 230 24.41909
#> Merc 280 18.69903
#> Merc 280C 19.19165
#> Merc 450SE 14.17216
#> Merc 450SL 15.59957
#> Merc 450SLC 15.74222
#> Cadillac Fleetwood 12.03401
#> Lincoln Continental 10.93644
#> Chrysler Imperial 10.49363
#> Fiat 128 27.77291
#> Honda Civic 29.89674
#> Toyota Corolla 29.51237
#> Toyota Corona 23.64310
#> Dodge Challenger 16.94305
#> AMC Javelin 17.73218
#> Camaro Z28 13.30602
#> Pontiac Firebird 16.69168
#> Fiat X1-9 28.29347
#> Porsche 914-2 26.15295
#> Lotus Europa 27.63627
#> Ford Pantera L 18.87004
#> Ferrari Dino 19.69383
#> Maserati Bora 13.94112
#> Volvo 142E 24.36827