Predicts the AMR phenotype in the test data set based on the best fit ML model.
predictML(fit, test_data)Best model fit, such as the output of fitBestModel()
The part of the pangenome data designated for ML model
testing. This can be the output of
rsample::testing(splitMLInputTibble(ml_input_tibble)).
Test data (tibble) with an added column for predicted phenotype labels
data(demo_ml_tibble)
data(demo_fit)
data_split <- splitMLInputTibble(demo_ml_tibble, split = c(1, 0), seed = 1)
predictML(demo_fit, rsample::testing(data_split))
#> # A tibble: 12 × 85
#> .pred_class .pred_Resistant .pred_Susceptible genome_id
#> <fct> <dbl> <dbl> <chr>
#> 1 Susceptible 0.00000330 1.000 623.1384
#> 2 Susceptible 0.00245 0.998 623.1309
#> 3 Resistant 0.715 0.285 623.1284
#> 4 Resistant 0.715 0.285 623.1393
#> 5 Resistant 0.715 0.285 623.1334
#> 6 Resistant 0.715 0.285 623.1281
#> 7 Resistant 0.715 0.285 623.1696
#> 8 Resistant 0.715 0.285 623.1686
#> 9 Susceptible 0.00688 0.993 623.1827
#> 10 Resistant 0.715 0.285 623.1297
#> 11 Susceptible 0.00485 0.995 623.1372
#> 12 Susceptible 0.00198 0.998 623.1812
#> # ℹ 81 more variables: genome_drug.resistant_phenotype <fct>, group_100 <dbl>,
#> # group_10000 <dbl>, group_10001 <dbl>, group_10002 <dbl>, group_10003 <dbl>,
#> # group_10004 <dbl>, group_10005 <dbl>, group_10006 <dbl>, group_10007 <dbl>,
#> # group_10008 <dbl>, group_10009 <dbl>, group_10010 <dbl>, group_10011 <dbl>,
#> # group_10012 <dbl>, group_10013 <dbl>, group_10014 <dbl>, group_10015 <dbl>,
#> # group_10016 <dbl>, group_10017 <dbl>, group_10018 <dbl>, group_10019 <dbl>,
#> # group_10020 <dbl>, group_10021 <dbl>, group_10022 <dbl>, …