I'm a bit confused, and maybe someone can solve my problem of understanding.
I've classified some imagery and performed my accuracy assessment. While following the usage guideline for ee.FeatureCollection.ErrorMatrix() the final array containing the Error Matrix values seems to be transposed.
As row totals I'm getting the reference/validation totals and not as wanted the classification totals.
I'm using the following statement:
basescene_error_matrix = validation.errorMatrix(actual='validation', predicted='classification')
I'm aware of the possibilty of just switching my inputs for validation.errorMatrix()
,
however, this would mean, that the function parameters are inversed.
Attached you see an example error matrix I created using the command. However, the row total values match my validation sample size. Yet, they should be within the column totals.
My Validation samples sizes are as follows:
Class | Sample size |
---|---|
Informal | 45 |
Formal | 43 |
Industrial | 35 |
Roads | 42 |
Vacant Land | 100 |
Vegetation | 53 |
Waterbodies | 32 |
sklearn.metrics.confusion_matrix
. Also, did you split train/test samples? Why are you using unbalanced sample sizes for your analysis? That could mess your classification model