Bab 4
Classification: Basic Concepts,
Decision Trees & Model Evaluation
Part 1
Classification With Decision tree
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Classification: Definition
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Example of Classification Task
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General Approach for Building
Classification Model
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Classification Techniques
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Example of Decision Tree
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Another Example of Decision Tree
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Decision Tree Classification Task
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Apply Model to Test Data
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Decision Tree Classification Task
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Decision Tree Induction
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General Structure of Hunt’s Algorithm
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Hunt’s Algorithm
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Design Issues of Decision Tree Induction
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Methods for Expression Test Conditions
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Test Condition for Nominal Attributes
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Test Condition for Ordinal Attributes
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Test Condition for Continues Attributes
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Splitting Based on Continues Attributes
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How to Determine the Best Split / 1
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How to Determine the Best Split / 2
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Measures of Node Impurity
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Finding the Best Split / 1
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Finding the Best Split / 2
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Measure of Impurity: GINI
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Computing GINI Index of a Single Node
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Computing GINI Index for a Collection of Nodes
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Binary Attributes: Computing GINI Index
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Categorical Attributes: Computing GINI Index
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Continuous Attributes: Computing GINI Index / 1
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Continuous Attributes: Computing GINI Index / 2
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Measure of Impurity: Entropy
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Computing Entropy of a Single Node
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Computing information Gain After Splitting
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Problems with Information Gain
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Gain Ratio
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Measure of Impurity: Classification Error
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Computing Error of a Single Node
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Comparison among Impurity Measures
For binary (2-class) classification problems
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Misclassification Error vs Gini index
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Example: C4.5
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•
•
•
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Simple depth-first construction.
Uses Information Gain
Sorts Continuous Attributes at each node.
Needs entire data to fit in memory.
Unsuitable for Large Datasets.
 Needs out-of-core sorting.
• You can download the software from:
http://www.cse.unsw.edu.au/~quinlan/c4.5r8.tar.gz
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Scalable Decision Tree Induction / 1
• How scalable is decision tree induction?
 Particularly suitable for small data set
• SLIQ (EDBT’96 — Mehta et al.)
 Builds an index for each attribute and only class list and
the current attribute list reside in memory
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Scalable Decision Tree Induction / 2
• SLIQ
RID
Credit_rating
Age
Buys_computer
1
excellent
38
yes
2
excellent
26
yes
3
fair
35
no
4
excellent
49
no
0
Sample data for the class buys_computer
Credit_rating
RID
age
RID
RID
Buys_computer
node
excellent
1
26
2
1
yes
5
excellent
2
35
3
2
yes
2
excellent
4
38
1
3
no
3
fair
3
49
4
4
no
6
…
…
…
…
…
…
…
Disk-resident attribute lists
Memory-resident class list
1
2
3
4
5
6
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Decision Tree Based Classification
• Advantages




Inexpensive to construct
Extremely fast at classifying unknown records
Easy to interpret for small-sized tress
Accuracy is comparable to other classification
techniques for many data sets
• Practical Issues of Classification
 Underfitting and Overfitting
 Missing Values
 Costs of Classification
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