Scientific Reports

Table 11 Confusion matrix for extra tree (et), light gradient boosting machine(lightgbm), and random forest classifier (rf).

From: Stacked encoding and AutoML-based identification of lead–zinc small open pit active mines around Rampura Agucha in Rajasthan state, India

	Predicted class
	LU/LC Classes	Crop Land	Fallow Land	Mining Area	Settlement	Vegetation	Water body
Extra tree classifier	Crop land	48	0	0	0	1	1
	Fallow land	2	53	0	1	2	0
	Mining area	0	0	59	1	0	3
	Settlement	0	0	2	58	0	1
	Vegetation	0	2	0	0	64	0
	Waterbody	1	0	1	0	0	56
Overall accuracy 0.94 Kappa coefficient 0.93

	Predicted class
	LU/LC classes	Crop land	Fallow land	Mining area	Settlement	Vegetation	Water body
Light gradient boosting machine classifier	Crop land	45	0	0	0	1	0
	Fallow land	2	52	0	1	2	0
	Mining area	0	0	59	1	0	3
	Settlement	0	0	2	58	0	1
	Vegetation	0	2	0	0	64	1
	Waterbody	1	0	1	0	0	56
Overall accuracy 0.937 Kappa coefficient 0.924

	Predicted class
	LU/LC classes	Crop land	Fallow land	Mining area	Settlement	Vegetation	Water body
Random forest classifier	Crop land	48	0	0	0	1	0
	Fallow land	2	52	0	1	2	0
	Mining area	0	0	59	1	0	3
	Settlement	0	0	2	58	0	1
	Vegetation	0	2	0	0	64	1
	Waterbody	1	0	1	0	0	56
Overall accuracy 0.9362 Kappa coefficient 0.923

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