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A partition-based framework for building and validating regression models.

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This study introduces a new framework for building regression models, enhancing feature selection by combining visualization and quantitative relevance ranking. This approach reduces the effort required for developing and refining predictive models in various domains.

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Area of Science:

  • Data Science
  • Machine Learning
  • Statistical Modeling

Background:

  • Regression models are crucial for prediction and analysis but automated methods struggle with domain knowledge integration.
  • Existing automated approaches for feature subset selection, identifying local structures, transformations, and variable interactions are limited.
  • Incorporating domain expertise into regression model building remains a challenge.

Purpose of the Study:

  • To present a novel framework for building regression models that overcomes limitations in automated feature selection and domain knowledge integration.
  • To enable a more comprehensive analysis of relationships between independent and dependent variables.
  • To facilitate interactive workflows for feature subset selection and model development.

Main Methods:

  • Combines qualitative analysis via visualization with quantitative relevance ranking for features and feature pairs.
  • Employs local approximation of conditional target distribution by partitioning feature domains into disjoint regions.
  • Enables visual investigation of local patterns and quantitative ranking with minimal structural assumptions.

Main Results:

  • The framework supports various model-building tasks, including validation and comparison.
  • An interactive workflow for feature subset selection is presented.
  • A real-world case study demonstrated the step-wise identification of a five-dimensional model for natural gas consumption.

Conclusions:

  • The proposed framework effectively addresses limitations in automated regression model building.
  • It facilitates a more intuitive and efficient process for feature selection and model development.
  • Domain experts reported significant effort reduction in building and improving regression models after deployment in the energy sector.