How does tree bagger handle NaN values
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In building a random forest classifier I have some features with a large amount of NaN values, but it is not clear to me how Tree Bagger handles these NaNs. I've seen quite a bit of documentation of how that is handled in other high level programming languages, but I don't see explicitly how this is done in Matlab. Can anyone point me in the right direction so I can understand the default settings for this or user specified settings?
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Puru Kathuria
2020-12-27
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General rules that are followed while NaN or missing values are encountered:
- Rule1: The algorithm simply discards the data points where all the features have NaN values and does not use them while training.
- Rule 2: If a data point have a few NaN feature values then the algorithm will find the split on the basis of valid values first.
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2020-2-7
2020-12-27
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