Decision Trees from large Databases: SLIQ

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<strong>SLIQ</strong> – Algorithm 1. Start Pre-sorting of the samples. 2. As long as the stop criterion has not been reached 1. For every attribute 1. Place all nodes into a class histogram. 2. Start evaluation of the splits. 2. Choose a split. 3. Update the decision tree; for each new node update its class list (nodes). Sawade/Landwehr/Prasse/Scheffer, Maschinelles Lernen 48
<strong>SLIQ</strong> (2nd Substep) • Evaluation of the splits. 1. For each node, and for all attributes 1. Construct a histogram (for each class the histogram saves the count of samples before and after the split). 2. For each attribute A 1. For each value v (traverse the attribute list for A) 1. Find the entry in the class list (provides the class and node). 2. Update the histogram for the node. 3. Assess the split (if its a maximum, record it!) Sawade/Landwehr/Prasse/Scheffer, Maschinelles Lernen 49
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: SLIQ: Example Sawade/Landwehr/Prass
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 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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