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articles/AMR_with_tidymodels.md
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@@ -332,9 +332,9 @@ predictions %>%
### **Conclusion**
In this post, we demonstrated how to build a machine learning pipeline
with the `tidymodels` framework and the `AMR` package. By combining
selector functions like
In this example, we demonstrated how to build a machine learning
pipeline with the `tidymodels` framework and the `AMR` package. By
combining selector functions like
[`aminoglycosides()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
and
[`betalactams()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
@@ -431,10 +431,9 @@ testing_data <- testing(split)
# Define the recipe
mic_recipe <- recipe(esbl ~ ., data = training_data) %>%
remove_role(genus, old_role = "predictor") %>% # Remove non-informative variable
step_mic_log2(all_mic_predictors()) #%>% # Log2 transform all MIC predictors
# prep()
step_mic_log2(all_mic_predictors()) # Log2 transform all MIC predictors
mic_recipe
prep(mic_recipe)
#>
#> ── Recipe ──────────────────────────────────────────────────────────────────────
#>
@@ -444,8 +443,11 @@ mic_recipe
#> predictor: 17
#> undeclared role: 1
#>
#> ── Training information
#> Training data contained 375 data points and no incomplete rows.
#>
#> ── Operations
#> • Log2 transformation of MIC columns: all_mic_predictors()
#> • Log2 transformation of MIC columns: AMC, AMP, TZP, CXM, FOX, ... | Trained
```
**Explanation:**
@@ -540,8 +542,8 @@ metrics
check the predictions with.
It appears we can predict ESBL gene presence with a positive predictive
value (PPV) of 92.1% and a negative predictive value (NPV) of 91.9 using
a simplistic logistic regression model.
value (PPV) of 92.1% and a negative predictive value (NPV) of 91.9%
using a simplistic logistic regression model.
### **Visualising Predictions**
@@ -574,20 +576,22 @@ predictions %>%
colour = correct)) +
scale_colour_manual(values = c(Right = "green3", Wrong = "red2"),
name = "Correct?") +
geom_point() +
geom_point() +
scale_y_continuous(labels = function(x) paste0(x * 100, "%"),
limits = c(0.5, 1)) +
theme_minimal()
```
![](AMR_with_tidymodels_files/figure-html/unnamed-chunk-15-1.png) \###
**Conclusion**
![](AMR_with_tidymodels_files/figure-html/unnamed-chunk-15-1.png)
### **Conclusion**
In this example, we showcased how the new `AMR`-specific recipe steps
simplify working with `<mic>` columns in `tidymodels`. The
[`step_mic_log2()`](https://amr-for-r.org/reference/amr-tidymodels.md)
transformation converts ordered MICs to log2-transformed numerics,
improving compatibility with classification models.
transformation converts MICs (with or without operators) to
log2-transformed numerics, improving compatibility with classification
models.
This pipeline enables realistic, reproducible, and interpretable
modelling of antimicrobial resistance data.
@@ -762,7 +766,7 @@ fitted_workflow_time <- resistance_workflow_time %>%
# Make predictions
predictions_time <- fitted_workflow_time %>%
predict(test_time) %>%
bind_cols(test_time)
bind_cols(test_time)
# Evaluate model
metrics_time <- predictions_time %>%