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Exploring Income Determinants Using Decision Trees at Warsaw University

Analyzing household incomes in Poland from 2000-2010, focusing on factors affecting income, including education, family type, and economic group. The study uses decision trees to uncover trends and patterns in income data, with a particular emphasis on the significance of education as a key determinant. The research, conducted at Warsaw University of Life Sciences, provides insights into income trends over the years and highlights the evolving importance of various attributes in predicting income levels. The study also identifies the most influential factors and offers recommendations for understanding income distribution dynamics. Key findings and methodological approaches are discussed, drawing on concepts like entropy, gain, and information gain, to enhance the understanding of income determinants.

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Exploring Income Determinants Using Decision Trees at Warsaw University

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  1. Discovering and analyzingincomedeterminantsusingdecisiontrees Krzysztof KarpioPiotr ŁukasiewiczArkadiusz OrłowskiTomasz Ząbkowski Warsaw University of Life Sciences - SGGW

  2. Data • Householdsincomes • Poland • Years: 2000 – 2010 • „Budżety Gospodarstw Domowych” - GUS • About 36 000 households in eachyear • Householdincome / Number of earners • Real income (based on prices in 2008). Warsaw University of Life Sciences - SGGW

  3. Conditionalatributes FEMALE  MALEMean: 17.3  20.4 kPLN • Sex of a family head • Education of a family head • Age of a family head • Economicgroup of a household • Family type • Number of persons in a household • Number of children • Number of earners • Class of place of residence • Voivodeship VILLAGE – CITYMean: 16.6  26.3 kPLN PODKARPACKIE – MAZOWIECKIEMean: 14.7  23.6 kPLN Warsaw University of Life Sciences - SGGW

  4. Incomes 2008 8 kPLN 16 kPLN 45 kPLN LOW 7% AVERAGE40% MODERATE48% HIGH 5% Warsaw University of Life Sciences - SGGW

  5. Method • Decisiontree • Entropy • Gain Rudolf Clausius (1822 – 1888) Warsaw University of Life Sciences - SGGW

  6. Attributestree 2008 at least a secondary marriedcouple pensioners Warsaw University of Life Sciences - SGGW

  7. Treenodes and leaves Attributes 2000 - 2010 Education Family type Economicgroup Number of earners Class of place of residence Education Family type Economicgroup Number of earners Class of place of residence Not relevant 2000 - 2010 • Sex of a family head • Age • Number of persons • Number of children • Voivodeship. Warsaw University of Life Sciences - SGGW

  8. Information Gain GAIN 0,01 Warsaw University of Life Sciences - SGGW

  9. 2-classes (high income) Warsaw University of Life Sciences - SGGW

  10. 2-classes (lowincome) Warsaw University of Life Sciences - SGGW

  11. Efficiency of trees High income Lowincome Warsaw University of Life Sciences - SGGW

  12. Summary • The most importantattribute: Education • HigherEducation (BA & MA) prefered • Importantattributes: Education, Family Type(marriage), EconomicGroup(pensioners), Resindence (big cities), Number of Earners(1 or 2) • Evolution of attributes (2000-2010) • Education - stable, the most important • Numer of Earners– decreasingimportance • EconomicGroup– increasingimportance • Family Type– the weakest but noticableimportance • Lack of relevance of: Sex, Age, Voivodeship to be continued ….. Warsaw University of Life Sciences - SGGW

  13. ThankYou REFERENCES • Quinlan, J. R. „C4.5: Programs for Machine Learning”, Morgan Kaufmann, (1993) Los Altos • Kemal Polat, SalihGunes, „A novel hybrid intelligent method based on C4.5 decision treeclassifier and one-against-all approach for multi-classclassification problems”, Expert Systems with Applications 36 (2009) 1587 • . THANK YOU Warsaw University of Life Sciences - SGGW

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