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(v3.0.1.9009) tidymodels vignette

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dr. M.S. (Matthijs) Berends
2025-12-23 11:02:26 +01:00
parent e9cf3d5572
commit 35debe25ee
3 changed files with 7 additions and 8 deletions
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vignettes/AMR_with_tidymodels.Rmd
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@@ -267,10 +267,9 @@ 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)
```
**Explanation:**
@@ -331,7 +330,7 @@ metrics
- `predict()`: Produces predictions for unseen test data.
- `metric_set()`: Allows evaluating multiple classification metrics. This will make `our_metrics` to become a function that we can use to check the predictions with.
It appears we can predict ESBL gene presence with a positive predictive value (PPV) of `r round(metrics[metrics$.metric == "ppv", ]$.estimate, 3) * 100`% and a negative predictive value (NPV) of `r round(metrics[metrics$.metric == "npv", ]$.estimate, 3) * 100` using a simplistic logistic regression model.
It appears we can predict ESBL gene presence with a positive predictive value (PPV) of `r round(metrics[metrics$.metric == "ppv", ]$.estimate, 3) * 100`% and a negative predictive value (NPV) of `r round(metrics[metrics$.metric == "npv", ]$.estimate, 3) * 100`% using a simplistic logistic regression model.
### **Visualising Predictions**
@@ -369,7 +368,7 @@ predictions %>%
### **Conclusion**
In this example, we showcased how the new `AMR`-specific recipe steps simplify working with `<mic>` columns in `tidymodels`. The `step_mic_log2()` transformation converts ordered MICs to log2-transformed numerics, improving compatibility with classification models.
In this example, we showcased how the new `AMR`-specific recipe steps simplify working with `<mic>` columns in `tidymodels`. The `step_mic_log2()` 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.