Learning Qualitative Models from Numerical Data: Extended abstract

Qualitative models are predictive models that describe how changes in values of input variables affect the output variable in qualitative terms, e.g. increasing or decreasing. We describe Pade, a new method for qualitative learning which estimates partial derivatives of the target function from training data and uses them to induce qualitative models of the target function. We formulated three methods for computation of derivatives, all based on using linear regression on local neighbourhoods. The methods were empirically tested on artificial and real-world data. We also provide a case study which shows how the developed methods can be used in practice.

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