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README.md

@@ -69,3 +69,14 @@ def preproc_transforms(examples):
 ds = ds.map(preproc_transforms, batched=True)
 ds['pixel_values'][405]
 ```
+# Geographic splits within the training set
+![Geographic clusters](https://huggingface.co/datasets/mpg-ranch/leafy_spurge/resolve/main/doc_figures/train_clusters.png){:height="50%" width="50%"}
+
+We gathered ground truth data at multiple sites, and observations within a site were geographically clustered. We suggest using the cluster feature as a means to establish holdout sets for cross-validated hyperparameter tuning. This will simluate model performance when classifying leafy spurge at new sites (such as those of the test set). You can filter by cluster metadata as follows:
+
+```python
+holdout_sets = [[0], [1], [2], [4], [5], [6,7], [8]] #spatial clusters of ground truth define holdout sets; 6 and 7
+
+set_0 = ds.filter(lambda example: example['cluster'] in holdout_sets[0])
+set_0['cluster']
+```