CHAPTER 9: Decision Trees des corrected and new slides added by Ch. Eick
CHAPTER 9:
Decision Trees
Slides corrected and new slides added by Ch. Eick
Lecture Notes for E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1)2
Tree Uses Nodes, and Leaves
3
Divide and Conquer
Internal decision nodes Univariate: Uses a single attribute, xi
Numeric xi : Binary split : xi > wm
Discrete xi : n-way split for n possible values Multivariate: Uses all attributes, x
Leaves Classification: Class labels, or proportions Regression: Numeric; r average, or local fit
Learning is greedy; find the best split recursively (Breiman et al, 1984; Quinlan, 1986, 1993) without backtracking (decision taken are final)
Side Discussion “Greedy Algorithms”
Fast and therefore attractive to solve NP-hard and other problems with high complexity. Later decisions are made in the context of decision selected early dramatically reducing the size of the search space.
They do not backtrack: if they make a bad decision (based on local criteria), they never revise the decision.
They are not guaranteed to find the optimal solutions, and sometimes can get deceived and find really bad solutions.
In spite of what is said above, a lot successful and popular algorithms in Computer Science are greedy algorithms.
Greedy algorithms are particularly popular in AI and Operations Research.
Popular Greedy Algorithms: Decision Tree Induction,…
Lecture Notes for E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1)5
Classification Trees (ID3, CART, C4.5) For node m, Nm instances reach m, Ni
m belong to Ci
Node m is pure if pim is 0 or 1
Measure of impurity is entropy
m
imi
mi NN
pm,CP̂ x|
K
i
im
imm pp
12logI
Lecture Notes for E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1)6
Best Split
If node m is pure, generate a leaf and stop, otherwise split and continue recursively
Impurity after split: Nmj of Nm take branch j. Nimj belong
to Ci
Find the variable and split that min impurity (among all variables -- and split positions for numeric variables)
mj
imji
mji N
Npj,m,CP̂ x|
K
i
imj
imj
n
j m
mjm pp
N
N
12
1
logI'
skip
Tree Induction
Greedy strategy. Split the records based on an attribute test that
optimizes certain criterion.
Issues Determine how to split the records
How to specify the attribute test condition? How to determine the best split?
Determine when to stop splitting
Information Gain vs. Gain Ratiosale custId car age city newCar
c1 taurus 27 sf yesc2 van 35 la yesc3 van 40 sf yesc4 taurus 22 sf yesc5 merc 50 la noc6 taurus 25 la no
D=(2/3,1/3)
D1(1,0) D2(1/3,2/3)
D=(2/3,1/3)
D1(0,1) D2(2/3,1/3) D3(1,0)
D=(2/3,1/3)
D1(1,0) D2(0,1) D3(1,0) D4(1,0) D5(1,0) D6(0,1)
city=sf city=la
car=merc car=taurus car=van
age=22 age=25 age=27 age=35 age=40 age=50
Gain(D,city=)= H(1/3,2/3) – ½ H(1,0) – ½ H(1/3,2/3)=0.45
Gain(D,car=)= H(1/3,2/3) – 1/6 H(0,1) – ½ H(2/3,1/3) – 1/3 H(1,0)=0.45
Gain(D,age=)= H(1/3,2/3) – 6*1/6 H(0,1) = 0.90
G_Ratio_pen(city=)=H(1/2,1/2)=1
G_Ratio_pen(age=)=log2(6)=2.58
G_Ratio_pen(car=)=H(1/2,1/3,1/6)=1.45
Result:I_Gain_Ratio:city>age>car
Result:I_Gain:age > car=city
How to determine the Best Split?
OwnCar?
C0: 6C1: 4
C0: 4C1: 6
C0: 1C1: 3
C0: 8C1: 0
C0: 1C1: 7
CarType?
C0: 1C1: 0
C0: 1C1: 0
C0: 0C1: 1
StudentID?
...
Yes No Family
Sports
Luxury c1c10
c20
C0: 0C1: 1
...
c11
Before Splitting: 10 records of class 0,10 records of class 1
Before: E(1/2,1/2)
After: 4/20*E(1/4,3/4) + 8/20*E(1,0) + 8/20*E(1/8,7/8)Gain: Before-AfterPick Test that has the highest gain!Remark: E stands for Gini, Entropy(H), Impurity(1-maxc(P(c )), Gain-ratio
Entropy and Gain ComputationsAssume we have m classes in our classification problem. A test S
subdivides the examples D= (p1,…,pm) into n subsets D1 =(p11,…,p1m) ,…,Dn =(p11,…,p1m). The qualify of S is evaluated using Gain(D,S) (ID3) or GainRatio(D,S) (C5.0):Let H(D=(p1,…,pm))= i=1 (pi log2(1/pi)) (called the entropy
function)Gain(D,S)= H(D) i=1 (|Di|/|D|)*H(Di)
Gain_Ratio(D,S)= Gain(D,S) / H(|D1|/|D|,…, |Dn|/|D|)
Remarks: |D| denotes the number of elements in set D.D=(p1,…,pm) implies that p1+…+ pm =1 and indicates that of the |D|
examples p1*|D| examples belong to the first class, p2*|D| examples belong to the second class,…, and pm*|D| belong the m-th (last) class.
H(0,1)=H(1,0)=0; H(1/2,1/2)=1, H(1/4,1/4,1/4,1/4)=2, H(1/p,…,1/p)=log2(p).
C5.0 selects the test S with the highest value for Gain_Ratio(D,S), whereas ID3 picks the test S for the examples in set D with the highest value for Gain (D,S).
m
n
Information Gain vs. Gain Ratiosale custId car age city newCar
c1 taurus 27 sf yesc2 van 35 la yesc3 van 40 sf yesc4 taurus 22 sf yesc5 merc 50 la noc6 taurus 25 la no
D=(2/3,1/3)
D1(1,0) D2(1/3,2/3)
D=(2/3,1/3)
D1(0,1) D2(2/3,1/3) D3(1,0)
D=(2/3,1/3)
D1(1,0) D2(0,1) D3(1,0) D4(1,0) D5(1,0) D6(0,1)
city=sf city=la
car=merc car=taurus car=van
age=22 age=25 age=27 age=35 age=40 age=50
Gain(D,city=)= H(1/3,2/3) – ½ H(1,0) – ½ H(1/3,2/3)=0.45
Gain(D,car=)= H(1/3,2/3) – 1/6 H(0,1) – ½ H(2/3,1/3) – 1/3 H(1,0)=0.45
Gain(D,age=)= H(1/3,2/3) – 6*1/6 H(0,1) = 0.90
G_Ratio_pen(city=)=H(1/2,1/2)=1
G_Ratio_pen(age=)=log2(6)=2.58
G_Ratio_pen(car=)=H(1/2,1/3,1/6)=1.45
Result:I_Gain_Ratio:city>age>car
Result:I_Gain:age > car=city
Splitting Continuous Attributes
Different ways of handling Discretization to form an ordinal categorical attribute
Static – discretize once at the beginning Dynamic – ranges can be found by equal interval
bucketing, equal frequency bucketing(percentiles), clustering, or supervised
clustering.
Binary Decision: (A < v) or (A v) consider all possible splits and finds the best cut v can be more compute intensive
Lecture Notes for E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1)13
Stopping Criteria for Tree Induction1. Grow entire tree
Stop expanding a node when all the records belong to the same class
Stop expanding a node when all the records have the same attribute values
2. Pre-pruning (do not grow complete tree)1. Stop when only x examples are left (pre-pruning)2. … other pre-pruning strategies
How to Address Overfitting in Decision Trees
The most popular approach: Post-pruningGrow decision tree to its entiretyTrim the nodes of the decision tree in a
bottom-up fashion If generalization error improves after
trimming, replace sub-tree by a leaf node.Class label of leaf node is determined from
majority class of instances in the sub-tree
Advantages Decision Tree Based Classification Inexpensive to construct Extremely fast at classifying unknown records Easy to interpret for small-sized trees Okay for noisy data Can handle both continuous and symbolic attributes Accuracy is comparable to other classification techniques for many
simple data sets Decent average performance over many datasets Kind of a standard—if you want to show that your “new” classification
technique really “improves the world” compare its performance against decision trees (e.g. C 5.0) using 10-fold cross-validation
Does not need distance functions; only the order of attribute values is important for classification: 0.1,0.2,0.3 and 0.331,0.332, and 0.333 is the same for a decision tree learner.
Disadvantages Decision Tree Based Classification
Relies on rectangular approximation that might not be good for some dataset
Selecting good learning algorihtm parameters (e.g. degree of pruning) is non-trivial
Ensemble techniques, support vector machines, and kNN might obtain higher accuracies for a specific dataset.
More recently, forests (ensembles of decision trees) have gained some popularity.
Error in Regressions TreesExamples are:(1,1.37), (2,-1.45), (2.1 -1.35), (2.2,-125)(7, 2.06), (8, 1.66)Error before split: 1/6(2*0**2+ 2*0.01 +2*0.04)In general: minimizes squared error!
Lecture Notes for E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1)19
Regression Trees
Error at node m:
After splitting:
t
tm
t
ttm
mt
mt mt
mm
mm
b
rbgbgr
NE
m:b
x
xx
xxx
1
otherwise0
node reaches if 1
2
X
t
tmj
t
ttmj
mjj
tmjt mj
t
mm
mjmj
b
rbgbgr
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jm:b
x
xx
xxx
1
otherwise0
branch and node reaches if 1
2
X
Error in Regressions TreesExamples are:(1,1.37), (2,-1.45), (2.1 -1.35), (2.2,-125)(7, 2.06), (8, 1.66)Error before split: 1/6(2*0**2+ 2*0.01 +2*0.04)
Lecture Notes for E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1)21
Model Selection in Trees:
Summary Regression Trees Regression trees work as decision trees except
Leafs carry numbers which predict the output variable instead of class label Instead of entropy/purity the squared prediction error serves as the
performance measure. Tests that reduce the squared prediction error the most are selected as node
test; test conditions have the form y>threshold where y is an independent variable of the prediction problem.
Instead of using the majority class, leaf labels are generated by averaging over the values of the dependent variable of the examples that are associated with the particular class node
Like decision trees, regression tree employ rectangular tessellations with a fixed number being associated with each rectangle; the number is the output for the inputs which lie inside the rectangle.
In contrast to ordinary regression that performs curve fitting, regression tries use averaging with respect to the output variable when making predictions.
Lecture Notes for E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1)23
Multivariate Trees