Abstract
Feature interaction is a crucial concept when it comes to machine
learning interpretability. These interactions make it easier for the
person using a machine learning tool to know how the model made its
prediction. Intrinsic models such as the Generalized linear models pull
their interpretability aspects from detecting feature interactions on
the data. However, these models do not search the whole sample space for
interactions and assume all interactions to the target feature are
identical. A hybrid model combining Rough Set and Generalized linear
models is proposed. Rough Set uses the concept of information
granulation and approximations of regions with meaningful information to
find feature interactions in the whole sample space. The detected
features will then be modeled onto a Generalized linear model for
prediction purposes. The dataset used in the experiment was drawn from
wunderground.com online weather site, specifically the Kariki Farm
online weather station. The proposed methodology for the research is the
CRISP-DM method.
Keywords: Machine interpretability, Rough Set, Generalized
linear model, weather prediction.