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 44
<strong>SLIQ</strong> (1st Substep) • Pre-sorting of the samples: 1. For each attribute: create an attribute list with columns for the value, sample-ID and class. 2. Create a class list with columns for the sample-ID, class and leaf node. 3. Iterate over all training samples: For each attribute Insert its attribute values, sample-ID and class (sorted by attribute value) into the attribute list. Insert the sample-ID, the class and the leaf node (sorted by sample-ID) into the class list. Sawade/Landwehr/Prasse/Scheffer, Maschinelles Lernen 45
Page 1 and 2: Decision Trees from large Databases
Page 3: Compare: Information Gain vs. Gini
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 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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