Why does the correlation matrix show the minority class when the majority class was selected
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If you don't mind could you send me the data you are mentioning or is the data available somewhere. From my perspective I am also interested to see what happens and how the correlation matrix looks like. It is important to be able to explain the step or how you should make any interpretation if you want to demonstrate your results to your colleagues.
Sven Van Poucke, MD, PhD
While I don't think your response answered my initial question it may have answered another question that I had and that was related to "one hot encoding". Are you saying that AutoModel automatically uses this technique when it sees categorical data? If that is so, there is not need to convert categorical data using a visual operator before uploading the data to AutoModel. Please elaborate and thanks
Altair Employeeyes. AM is doing this if the model cannot handle this and the model cannot handle nominal data. This is a very good go-to method to do - thats why AM is doing it.
Cheers,
Martin
If you don't mind could you send me the data you are mentioning or is the data available somewhere. From my perspective I am also interested to see what happens and how the correlation matrix looks like. It is important to be able to explain the step or how you should make any interpretation if you want to demonstrate your results to your colleagues.
Sven Van Poucke, MD, PhD
While I don't think your response answered my initial question it may have answered another question that I had and that was related to "one hot encoding". Are you saying that AutoModel automatically uses this technique when it sees categorical data? If that is so, there is not need to convert categorical data using a visual operator before uploading the data to AutoModel. Please elaborate and thanks
Altair Employeeyes. AM is doing this if the model cannot handle this and the model cannot handle nominal data. This is a very good go-to method to do - thats why AM is doing it.
Cheers,
Martin
All columns are set as regular attributes and then the nominal columns are one-hot encoded in automodel. So in the case of binary classification, all the values in target column related to one category is coded as 0 and the other category is coded as 1. So the output column has both classes represented either as 1 or 0. The category name you see in the attribute name is coded as 1 and another category is zero.
@IngoRM might provide more info if needed.