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Numeric boundaries are formed wrong for the states of input variables that have a limited number of unique values, which results in some missing and some 'overcrowded' scenarios:
Here is how the issue was solved in matlab:
The text was updated successfully, but these errors were encountered:
Another example where the dashboard defines states for the categorical variable wrong, misclassifying portions of data resulting in NaN scenarios (but no NaNs in the data).
[converting [1 2 3 4 5] to [1000 2000 3000 4000 5000] did not help] data10.csv
data.csv
Another example, where the most influential categorical variable with five unique values gets misclassified into 4 states, the 5th remaining empty.
Numeric boundaries are formed wrong for the states of input variables that have a limited number of unique values, which results in some missing and some 'overcrowded' scenarios:
Here is how the issue was solved in matlab:
The text was updated successfully, but these errors were encountered: