Decision Trees from large Databases: SLIQ

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Bagging – Learning Robust Trees • Bagging = Bootstrap aggregating • Bagging – Algorithm: Repeat k = 1 … M times : Generate a sample dataset L k from L. Compute model θ k for dataset L k . • Combine the M learned predictive models: Classification problems: Voting Regression problems: Mean value • Original reference: L.Breiman: “Bagging Predictors”. 1996 e.g. a model tree Sawade/Landwehr/Prasse/Scheffer, Maschinelles Lernen 72
Random Forests • Repeat: Draw randomly n samples, uniformly with replacement, from the training set. Randomly select m ′ < m features Learn decision tree (without pruning) • Classification: Maximum over all trees (Voting) • Regression: Average over all trees • Original reference: L. Breiman: ”Random Forests”. 2001 Bootstrap Sawade/Landwehr/Prasse/Scheffer, Maschinelles Lernen 73
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 and 24: Linear Regression • Regression tr
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: Bootstrapping - Example x = 3.12 0
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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