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Last update: February 19, 2014

 

Class Merging & Discretization

For classification and logistic regression the learning algorithms of GeneXproTools require a binary response variable, such as {0, 1} or {-1, 1}. This means that response variables with more than two different classes or values must be converted to binary (broadly defined as having two different numerical values, as GeneXproTools supports different class representations). Thus, combining the support for categorical variables with the tools for merging and discretizing the response, datasets with multiple classes and datasets with numerical responses (both continuous and discrete) can be used for creating classification and logistic regression models.

For datasets with multiple classes GeneXproTools allows you to set the singled out class to C1 for example, and then create models for the binomial classification task {class C1, not class C1}. Then single out another class and create models for it too, and so on until you've created models for all sub-tasks. The merging and discretization of the response variable takes place in the Class Merging & Discretization Window.



On the other hand, for datasets with a numerical response (loosely defined as having more than two different values), such as the output of a logistic regression model with continuous probability values between [0, 1], you can use the discretization function of GeneXproTools and easily convert the continuous output into a binary outcome, such as {0, 1} or {-1, 1}, by choosing 0.5 as the discretization threshold.




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