speed up format_classprobs() helper

[simonpcouch]

We're now good to go on the worst-case overhead of fit(), though this step in predict() introduces the last big slowdown causing the overhead in evaluation time in the causal estimate bootstrapping example. as_tibble() takes a bit of time, but the deprecation warning in map_dfr() especially slows us down.

With main dev:

library(tidymodels)

lr <- fit(logistic_reg(), Class ~ ., two_class_dat)

bench::mark(
  old = predict(lr, two_class_dat, type = "prob")
)
#> # A tibble: 1 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 old          3.01ms   3.12ms      305.    3.62MB     6.27

With this PR:

#> # A tibble: 1 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 new          1.32ms   1.35ms      729.   92.92KB     8.26

^{Created on 2023-03-20 with reprex v2.0.2}

[hfrick]

🏇

[github-actions]

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