Decision Trees from large Databases: SLIQ

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Linear Regression • Input: L = x 1 , y 1 , … , x n , y n • Desired result: a linear model, f x = w T x + c Normal vector Residuals Points along the Regression line are perpendicular to the normal vector. Sawade/Landwehr/Prasse/Scheffer, Maschinelles Lernen 64
Model <strong>Trees</strong> • <strong>Decision</strong> trees but with a linear regression model at each leaf node. Sawade/Landwehr/Prasse/Scheffer, Maschinelles Lernen .2 .3 .4 .5 .6 .7 .8 .9 1.0 1.1 1.2 1.3 65
Page 1 and 2: Decision Trees from large Databases
Page 3 and 4: Compare: Information Gain vs. Gini
Page 5 and 6: SLIQ (1st Substep) • Pre-sorting
Page 7 and 8: SLIQ: Example Sawade/Landwehr/Prass
Page 9 and 10: SLIQ (2nd Substep) • Evaluation o
Page 11 and 12: SLIQ - Algorithm 1. Start Pre-sorti
Page 13 and 14: SLIQ: Example Sawade/Landwehr/Prass
Page 15 and 16: SLIQ- Pruning • Minimum Descripti
Page 17 and 18: Pruning - MDL Example II • Which
Page 19 and 20: Regression Trees • ID3, C4.5, SLI
Page 21 and 22: Regression Trees • Goal: small qu
Page 23: Linear Regression • Regression tr
Page 27 and 28: Model Trees: Creation of new Test N
Page 29 and 30: Attributes with Costs • Example:
Page 31 and 32: Bootstrapping - Example x = 3.12 0
Page 33 and 34: Random Forests • Repeat: Draw ra
Page 35 and 36: Decision Tree - Advantages • Easy
Page 37: Summary of Decision Trees • Class

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