Datasets:

mpg-ranch
/

leafy_spurge

@@ -75,7 +75,7 @@ ds['pixel_values'][405]
 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
- #spatial clusters of ground truth define holdout sets; 6 and 7
 holdout_sets = [[0], [1], [2], [4], [5], [6,7], [8]]
 set_0 = ds.filter(lambda example: example['cluster'] in holdout_sets[0])

 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
+ #define holdout sets with ground truth clusters; 6 and 7 overlap geographically
 holdout_sets = [[0], [1], [2], [4], [5], [6,7], [8]]
 set_0 = ds.filter(lambda example: example['cluster'] in holdout_sets[0])