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Decision trees have the advantage that they are not sensitive to outliers or missing data, while other techniques are, e.g. regression or neural networks. What
Decision trees have the advantage that they are not sensitive to outliers or missing data, while other techniques are, e.g. regression or neural networks.
What options are available to data miners when there are missing values in the data?
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