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Linear Regression

In this tutorial, we demonstrate linear regression using PolyAnalyst, focusing on generating regression formulas and rules for categorical classification. We explore a CPU dataset and identify independent fields and target variables for analysis. The linear regression formula shows the relationship between various attributes, which can provide insights for predictions. We also examine a weather dataset to classify outlook conditions and predict outcomes, ensuring a comprehensive understanding of how to apply linear regression in different contexts.

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Linear Regression

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Presentation Transcript

  1. Linear Regression • Demo using PolyAnalyst • Generating Linear Regression Formula • Generating Regression Rules for Categorical classification

  2. CPU Data

  3. Select Data Source Type

  4. Select Source File

  5. Edit Project

  6. Explore by Linear Regression

  7. Select Independent Fields & Target

  8. Linear Regression • class = -65.4261 +0.0644143*MYCT +0.0142251*MMIN +0.00655274*MMAX +0.485249*CACH +1.18320*CHMAX

  9. Gain Chart

  10. Classification Rule • Demo using PolyAnalyst

  11. Weather Data

  12. Working with Attributes

  13. Classify

  14. Prediction Rule • 0.6224 < (+2.00797 +0.474537*if(outlook='overcast',1,0) -0.0165110*humidity -0.356277*if(windy,1,0))

  15. Confusion Matrix True Negative False Positive False Negative True Positive

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