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In my previous question (memoryerror in Supervised Random Forest Classification in Python sklearn) encounter further issue on random forest classification output. The python code runs fine, but the output is noise or invalid classification. I used randomly generated points and collected pixels from Maximum Likelihood Classification from ArcGIS 10.3 which was used as training dataset. The training dataset contains 1500 labels of land use-land cover. I am assuming it could be because point dataset may not contain enough pixels to represent each land-use, but i can't be sure.

Can you explain why the classification output is noise?

In my previous question (memoryerror in Supervised Random Forest Classification in Python sklearn) encounter further issue on random forest classification output. The python code runs fine, but the output is noise or invalid classification. I used randomly generated points and collected pixels from Maximum Likelihood Classification from ArcGIS 10.3 which was used as training dataset. The training dataset contains 1500 labels of land use-land cover. I am assuming it could be because point dataset may not contain enough pixels to represent each land-use, but i can't be sure.

Can you explain why the classification output is noise?

EDIT: The apparent noise output was due to switched row, col at the last sections of the code.

In my previous question (memoryerror in Supervised Random Forest Classification in Python sklearn encounter further issue on random forest classification output. The python code runs fine, but the output is noise or invalid classification. I used randomly generated points and collected pixels from Maximum Likelihood Classification from ArcGIS 10.3 which was used as training dataset. The training dataset contains 1500 labels of land use-land cover. I am assuming it could be because point dataset may not contain enough pixels to represent each land-use, but i can't be sure.

Can you explain why the classification output is noise?

In my previous question (memoryerror in Supervised Random Forest Classification in Python sklearn) encounter further issue on random forest classification output. The python code runs fine, but the output is noise or invalid classification. I used randomly generated points and collected pixels from Maximum Likelihood Classification from ArcGIS 10.3 which was used as training dataset. The training dataset contains 1500 labels of land use-land cover. I am assuming it could be because point dataset may not contain enough pixels to represent each land-use, but i can't be sure.

Can you explain why the classification output is noise?