Decision-trees

1. Induction of Decision Trees
Blaž Zupan and Ivan Bratko
magix.fri.uni-lj.si/predavanja/uisp

2. CS583, Bing Liu, UIC 2
An example application
• An emergency room in a hospital measures 17
variables (e.g., blood pressure, age, etc) of newly
admitted patients.
• A decision is needed: whether to put a new
patient in an intensive-care unit.
• Due to the high cost of ICU, those patients who
may survive less than a month are given higher
priority.
• Problem: to predict high-risk patients and
discriminate them from low-risk patients.

3. CS583, Bing Liu, UIC 3
Another application
• A credit card company receives thousands of
applications for new cards. Each application
contains information about an applicant,
– age
– Marital status
– annual salary
– outstanding debts
– credit rating
– etc.
• Problem: to decide whether an application should
approved, or to classify applications into two
categories, approved and not approved.

4. CS583, Bing Liu, UIC 4
Machine learning and our focus
• Like human learning from past experiences.
• A computer does not have “experiences”.
• A computer system learns from data, which
represent some “past experiences” of an
application domain.
• Our focus: learn a target function that can be
used to predict the values of a discrete class
attribute, e.g., approve or not-approved, and
high-risk or low risk.
• The task is commonly called: Supervised
learning, classification, or inductive learning.

5. CS583, Bing Liu, UIC 5
• Data: A set of data records (also called
examples, instances or cases) described by
– k attributes: A1, A2, … Ak.
– a class: Each example is labelled with a pre-
defined class.
• Goal: To learn a classification model from the
data that can be used to predict the classes
of new (future, or test) cases/instances.
The data and the goal

6. CS583, Bing Liu, UIC 6
An example: data (loan
application) Approved or not

7. CS583, Bing Liu, UIC 7
An example: the learning
task
• Learn a classification model from the data
• Use the model to classify future loan applications
into
– Yes (approved) and
– No (not approved)
• What is the class for following case/instance?

8. CS583, Bing Liu, UIC 8
Supervised vs. unsupervised
Learning
• Supervised learning: classification is seen as
supervised learning from examples.
– Supervision: The data (observations,
measurements, etc.) are labeled with pre-defined
classes. It is like that a “teacher” gives the classes
(supervision).
– Test data are classified into these classes too.
• Unsupervised learning (clustering)
– Class labels of the data are unknown
– Given a set of data, the task is to establish the
existence of classes or clusters in the data

9. CS583, Bing Liu, UIC 9
Supervised learning process:
two steps
 Learning (training): Learn a model using the
training data
 Testing: Test the model using unseen test
data to assess the model accuracy
,
casestestofnumberTotal
tionsclassificacorrectofNumber
=Accuracy

10. CS583, Bing Liu, UIC 10
What do we mean by learning?
• Given
– a data set D,
– a task T, and
– a performance measure M,
a computer system is said to learn from D to
perform the task T if after learning the
system’s performance on T improves as
measured by M.
• In other words, the learned model helps the
system to perform T better as compared to
no learning.

11. CS583, Bing Liu, UIC 11
An example
• Data: Loan application data
• Task: Predict whether a loan should be
approved or not.
• Performance measure: accuracy.
No learning: classify all future applications (test
data) to the majority class (i.e., Yes):
Accuracy = 9/15 = 60%.
• We can do better than 60% with learning.

12. CS583, Bing Liu, UIC 12
Fundamental assumption of
learning
Assumption: The distribution of training
examples is identical to the distribution of
test examples (including future unseen
examples).
• In practice, this assumption is often violated
to certain degree.
• Strong violations will clearly result in poor
classification accuracy.
• To achieve good accuracy on the test data,
training examples must be sufficiently
representative of the test data.

13. The worst pH value
at ICU
The worst active partial
thromboplastin time
PH_ICU and APPT_WORST are exactly the two factors
(theoretically) advocated to be the most important ones in
the study by Rotondo et al., 1997.
Analysis of Severe Trauma
Patients Data
Death
0.0 (0/15)
Well
0.82 (9/11)
Death
0.0 (0/7)
APPT_WORST
Well
0.88 (14/16)
PH_ICU
<78.7 >=78.7
>7.33<7.2
7.2-7.33

14. Tree induced by Assistant Professional
Interesting: Accuracy of this tree compared to medical
specialists
Breast Cancer Recurrence
no rec 125
recurr 39
recurr 27
no_rec 10
Tumor Size
no rec 30
recurr 18
Degree of Malig
< 3
Involved Nodes
Age
no rec 4
recurr 1
no rec 32
recurr 0
>= 3
< 15 >= 15 < 3 >= 3

15. Prostate cancer recurrence
Secondary Gleason GradeSecondary Gleason Grade
NoNo YesYesPSA LevelPSA Level StageStage
Primary Gleason GradePrimary Gleason Grade
NoNo YesYes
NoNo NoNo YesYes
1,2 3 4 5
≤14.9 >14.9 T1c,T2a,
T2b,T2c
T1ab,T3
2,3 4

16. CS583, Bing Liu, UIC 16
Introduction
• Decision tree learning is one of the most
widely used techniques for classification.
– Its classification accuracy is competitive with
other methods, and
– it is very efficient.
• The classification model is a tree, called
decision tree.
• C4.5 by Ross Quinlan is perhaps the best
known system. It can be downloaded from
the Web.

17. CS583, Bing Liu, UIC 17
The loan data (reproduced)
Approved or not

18. CS583, Bing Liu, UIC 18
A decision tree from the loan
data
 Decision nodes and leaf nodes (classes)

19. CS583, Bing Liu, UIC 19
Use the decision tree
No

20. CS583, Bing Liu, UIC 20
Is the decision tree unique?
 No. Here is a simpler tree.
 We want smaller tree and accurate tree.
 Easy to understand and perform better.
 Finding the best tree is
NP-hard.
 All current tree building
algorithms are heuristic
algorithms

21. An Example Data Set and
Decision Tree
yes
no
yes no
sunny rainy
no
med
yes
small big
big
outlook
company
sailboat
# Class
Outlook Company Sailboat Sail?
1 sunny big small yes
2 sunny med small yes
3 sunny med big yes
4 sunny no small yes
5 sunny big big yes
6 rainy no small no
7 rainy med small yes
8 rainy big big yes
9 rainy no big no
10 rainy med big no
Attribute

22. Classification
yes
no
yes no
sunny rainy
no
med
yes
small big
big
outlook
company
sailboat
# Class
Outlook Company Sailboat Sail?
1 sunny no big ?
2 rainy big small ?
Attribute

23. Induction of Decision Trees
• Data Set (Learning Set)
– Each example = Attributes + Class
• Induced description = Decision tree
• TDIDT
– Top Down Induction of Decision Trees
• Recursive Partitioning

24. Some TDIDT Systems
• ID3 (Quinlan 79)
• CART (Brieman et al. 84)
• Assistant (Cestnik et al. 87)
• C4.5 (Quinlan 93)
• See5 (Quinlan 97)
• ...
• Orange (Demšar, Zupan 98-03)

25. TDIDT Algorithm
• Also known as ID3 (Quinlan)
• To construct decision tree T from learning
set S:
– If all examples in S belong to some class C Then
make leaf labeled C
– Otherwise
• select the “most informative” attribute A
• partition S according to A’s values
• recursively construct subtrees T1, T2, ..., for the
subsets of S

26. Hunt’s Algorithm
Don’t
Cheat
Refund
Don’t
Cheat
Don’t
Cheat
Yes No
Refund
Don’t
Cheat
Yes No
Marital
Status
Don’t
Cheat
Cheat
Single,
Divorced
Married
Taxable
Income
Don’t
Cheat
< 80K >= 80K
Refund
Don’t
Cheat
Yes No
Marital
Status
Don’t
Cheat
Cheat
Single,
Divorced
Married

27. TDIDT Algorithm
• Resulting tree T is:
A
v1
T1 T2 Tn
v2 vn
Attribute A
A’s values
Subtrees

28. Another Example
# Class
Outlook Temperature Humidity Windy Play
1 sunny hot high no N
2 sunny hot high yes N
3 overcast hot high no P
4 rainy moderate high no P
5 rainy cold normal no P
6 rainy cold normal yes N
7 overcast cold normal yes P
8 sunny moderate high no N
9 sunny cold normal no P
10 rainy moderate normal no P
11 sunny moderate normal yes P
12 overcast moderate high yes P
13 overcast hot normal no P
14 rainy moderate high yes N
Attribute

29. Simple Tree
Outlook
Humidity WindyP
sunny
overcast
rainy
PN
high normal
PN
yes no

30. Complicated Tree
Temperature
Outlook Windy
cold moderate
hot
P
sunny rainy
N
yes no
P
overcast
Outlook
sunny rainy
P
overcast
Windy
PN
yes no
Windy
NP
yes no
Humidity
P
high normal
Windy
PN
yes no
Humidity
P
high normal
Outlook
N
sunny rainy
P
overcast
null

31. Attribute Selection Criteria
• Main principle
– Select attribute which partitions the learning set into
subsets as “pure” as possible
• Various measures of purity
– Information-theoretic
– Gini index
– X2
– ReliefF
– ...
• Various improvements
– probability estimates
– normalization
– binarization, subsetting

32. Information-Theoretic Approach
• To classify an object, a certain information is
needed
– I, information
• After we have learned the value of attribute A,
we only need some remaining amount of
information to classify the object
– Ires, residual information
• Gain
– Gain(A) = I – Ires(A)
• The most ‘informative’ attribute is the one that
minimizes Ires, i.e., maximizes Gain

33. Entropy
• The average amount of information I needed to
classify an object is given by the entropy measure
• For a two-class problem:
entropy
p(c1)

34. Residual Information
• After applying attribute A, S is partitioned
into subsets according to values v of A
• Ires is equal to weighted sum of the
amounts of information for the subsets

35. Triangles and Squares
# Shape
Color Outline Dot
1 green dashed no triange
2 green dashed yes triange
3 yellow dashed no square
4 red dashed no square
5 red solid no square
6 red solid yes triange
7 green solid no square
8 green dashed no triange
9 yellow solid yes square
10 red solid no square
11 green solid yes square
12 yellow dashed yes square
13 yellow solid no square
14 red dashed yes triange
Attribute

36. Triangles and Squares
.
.
.
.
.
.
# Shape
Color Outline Dot
1 green dashed no triange
2 green dashed yes triange
3 yellow dashed no square
4 red dashed no square
5 red solid no square
6 red solid yes triange
7 green solid no square
8 green dashed no triange
9 yellow solid yes square
10 red solid no square
11 green solid yes square
12 yellow dashed yes square
13 yellow solid no square
14 red dashed yes triange
Attribute
Data Set:
A set of classified objects

37. Entropy
• 5 triangles
• 9 squares
• class probabilities
• entropy
.
.
.
.
.
.

38. Entropy
reduction
by
data set
partitioning
.
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.
..
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.
.
.
Color?
red
yellow
green

39. Entropijavrednosti
atributa
.
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.
..
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Color?
red
yellow
green

40. InformationGain
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.
..
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Color?
red
yellow
green

41. Information Gain of The Attribute
• Attributes
– Gain(Color) = 0.246
– Gain(Outline) = 0.151
– Gain(Dot) = 0.048
• Heuristics: attribute with the highest gain is
chosen
• This heuristics is local (local minimization of
impurity)

42. .
.
.
.
.
.
..
.
.
.
.
Color?
red
yellow
green
Gain(Outline) = 0.971 – 0 = 0.971 bits
Gain(Dot) = 0.971 – 0.951 = 0.020 bits

43. .
.
.
.
.
.
..
.
.
.
.
Color?
red
yellow
green
.
.
Outline?
dashed
solid
Gain(Outline) = 0.971 – 0.951 = 0.020 bits
Gain(Dot) = 0.971 – 0 = 0.971 bits

44. .
.
.
.
.
.
..
.
.
.
.
Color?
red
yellow
green
.
.
dashed
solid
Dot?
no
yes
.
.
Outline?

45. Decision Tree
Color
Dot Outlinesquare
red
yellow
green
squaretriangle
yes no
squaretriangle
dashed solid
.
.
.
.
.
.

46. A Defect of Ires
• Ires favors attributes with many values
• Such attribute splits S to many subsets, and if
these are small, they will tend to be pure anyway
• One way to rectify this is through a corrected
measure of information gain ratio.

47. Information Gain Ratio
• I(A) is amount of information needed to
determine the value of an attribute A
• Information gain ratio

48. InformationGainRatio
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..
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.
Color?
red
yellow
green

49. Information Gain and
Information Gain Ratio
A |v(A)| Gain(A) GainRatio(A)
Color 3 0.247 0.156
Outline 2 0.152 0.152
Dot 2 0.048 0.049

50. Gini Index
• Another sensible measure of impurity
(i and j are classes)
• After applying attribute A, the resulting Gini
index is
• Gini can be interpreted as expected error rate

51. Gini Index
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.

52. GiniIndexforColor
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..
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Color?
red
yellow
green

53. Gain of Gini Index

54. Three Impurity Measures
A Gain(A) GainRatio(A) GiniGain(A)
Color 0.247 0.156 0.058
Outline 0.152 0.152 0.046
Dot 0.048 0.049 0.015
• These impurity measures assess the effect of a
single attribute
• Criterion “most informative” that they define is
local (and “myopic”)
• It does not reliably predict the effect of several
attributes applied jointly