Update README.md

README.md

@@ -212,7 +212,7 @@ KMeans splits the data into four roughly equal blobs with significant overlap in
 
 ![12_48_Separate_scatter_plots](https://cdn-uploads.huggingface.co/production/uploads/69d8c774af594a45bf54cc48/GmVq3G3FJO2ucpXWGywAl.png)
 
-DBSCAN classifies the vast majority of points as noise, forming only one meaningful cluster. High dimensionality makes distance-based density estimation ineffective on this dataset.
+DBSCAN classifies the vast majority of points as noise, forming 7 clusters. High dimensionality makes distance-based density estimation ineffective on this dataset.
 
 
 ![13_48_Separate_scatter_plots](https://cdn-uploads.huggingface.co/production/uploads/69d8c774af594a45bf54cc48/j6b9SMso4DovtAbKgqnSj.png)
@@ -324,6 +324,13 @@ Each bin shows a clean salary range with minimal overlap at the boundaries, conf
 
 Same 253-feature matrix as regression, with a stratified 80/20 train/test split.
 
+### Precision vs. Recall & False Positives vs. False Negatives
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+**Recall is prioritised over precision** in this task. Misclassifying a developer into a lower salary tier (a false negative) carries real-world cost — under-negotiation, poor benchmarking, missed career leverage — whereas a false positive (over-predicting a tier) is relatively benign.
+
+**False Negatives are more critical than False Positives.** Predicting "Mid" when a developer is truly "High" or "Very High" obscures their earning potential. For this reason, evaluation uses **weighted F1-score**, which balances precision and recall across all four classes with particular attention to recall in the minority tiers (Low and Very High).
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+
 ### Results
 
 | Model | Accuracy | F1 (weighted) |