Data Science Asked on October 30, 2020
I am trying to classify satellite based images by creating a region of interest and then classifying according to it.
I am using a Jupyter notebook using python to do that.
I used a Random forest classifier and got a nice model and result, but the problem is that the image is "over classified" meaning that all the pixels et value and force to be classified.
I would like to define level of similarity that a pixel has to have in order to be classified, otherwise, it will not get any class.
For example, the black suppose to be asphalt:
However, in the RGB, you can see it’s not asphalt:
Is there any way to define in random forest or any other algorithm "level os similarity"? (For example something similar to n-D angle to match pixels to reference like ised in SAM, but under random forest, or another algorithm that allows define that)
SAM- https://www.harrisgeospatial.com/docs/SpectralAngleMapper.html
My end goal: to get "unclassified" values based on similarity level to the calibration data
It seems you can use RandomForest to get probabilities of being in both class by using predict_proba(X)
.
You could just get thoses probs, get the higher one (which is currently the class assigned to your data), and manually set a threshold, for example setting that if the most probable class is at less than 75%, then you manually classifies it as "Unknown"
Correct answer by BeamsAdept on October 30, 2020
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