Decision Trees from large Databases: SLIQ

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CART- Example • Which split is better? L = [2,3,4,10,11,12] SSE L = 100 ja nein SSE L = y − y 2 x,y ∈L where y = 1 L x 1 x 2 L = [2,3,4,10,11,12] ja nein L 1 = [2,3] L 2 =[4,10,11,12] L 1 =[2,4,10] L 2 =[3,11,12] SSE L 1 = 0,5 SSE L 2 = 38,75 SSE L 1 = 34,67 SSE L 2 = 48,67 x,y ∈L y Sawade/Landwehr/Prasse/Scheffer, Maschinelles Lernen 62
Linear Regression • Regression trees: constant prediction at every leaf node. • Linear Regression: Global model of a linear dependence between x and y. • Standard method. • Useful by itself and it serves as a building block for Model trees. Sawade/Landwehr/Prasse/Scheffer, Maschinelles Lernen 63
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: Regression Trees • Goal: small qu
Page 25 and 26: Model Trees • Decision trees but
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

Decision Trees from large Databases: SLIQ

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