don't use check_outcome() when using sparklyr

[EmilHvitfeldt]

To close #671

library(sparklyr)
#> 
#> Attaching package: 'sparklyr'
#> The following object is masked from 'package:stats':
#> 
#>     filter
library(parsnip)

sc <- spark_connect("local")
iris_tbl <- sdf_copy_to(sc, iris)

decision_tree(engine = "spark") %>%
  set_mode("classification") %>%
  fit(Species ~ Sepal_Length + Petal_Length, iris_tbl)
#> parsnip model object
#> 
#> Formula: Species ~ Sepal_Length + Petal_Length
#> 
#> DecisionTreeClassificationModel (uid=decision_tree_classifier__6a7e32a5_0707_461f_bfe5_d825c6b6f9f1) of depth 5 with 17 nodes

spark_disconnect(sc)

^{Created on 2022-03-11 by the reprex package (v2.0.1)}

Should I add a corresponding test in https://github.com/tidymodels/extratests?

[juliasilge]

Unless I am misunderstanding, won't the existing Spark tests cover this?

[topepo]

The existing tests should catch this (since they are currently failing).

[github-actions]

This pull request has been automatically locked. If you believe you have found a related problem, please file a new issue (with a reprex: https://reprex.tidyverse.org) and link to this issue.