1. Classification and Regression Trees or CART
KH Wong
Classification CART v9d4

2. Classification CART v9d4
We will learn : the Classification and Regression Tree ( CART) ( or Decision Tree)
CART (Classification and Regression Trees) → uses Gini Index(Classification) as metric.
Other approaches: ID3 (Iterative Dichotomiser 3) → uses Entropy function and Information gain as metrics.
Classification CART v9d4

3. To build the tree you need training data
You should have enough data for training. It is a supervised learning algorithm
Divide the whole training data (100%) into:
Training set (30%): for training your classifier  
Validation set (10%): for tuning the parameters
Test set (20%): for test the performance of your classifier  
Classification CART v9d4

4. CART can preform classification or regression functions
So when to use classification or regression
Classification trees : Outputs are class symbols not real numbers. E.g. high, medium, low etc.
Regression trees : Outputs are target variables (real numbers): E.g , etc.
See
Classification CART v9d4

5. Classification tree approaches
Famous trees are ID3, C4.5 and CART.
What are the differences ?
We only learn CART here.
Classification CART v9d4

6. Classification CART v9d4
Decision tree diagram
Classification CART v9d4

7. Common terms used with Decision trees
Root Node: It represents entire population or sample and this further gets divided into two or more homogeneous sets.
Splitting: It is a process of dividing a node into two or more sub-nodes.
Decision Node: When a sub-node splits into further sub-nodes, then it is called decision node.
Leaf/ Terminal Node: Nodes do not split is called Leaf or Terminal node.
Pruning: When we remove sub-nodes of a decision node, this process is called pruning. You can say opposite process of splitting.
Branch / Sub-Tree: A sub section of entire tree is called branch or sub-tree.
Parent and Child Node: A node, which is divided into sub-nodes is called parent node of sub-nodes whereas sub-nodes are the child of parent node.
Classification CART v9d4

8. CART Model Representation
Root
node
Attribute
(variables)
2 inner nodes: 1 root node ,1 leaf node
CART is a binary tree.
Each root node represents a single input variable (x) and a split point on that variable (assuming the variable is numeric).
The leaf nodes of the tree contain an output variable (y) which is used to make a prediction.
Given a dataset with two inputs (x) of height in centimeters and weight in kilograms the output of sex as male or female, below is a crude example of a binary decision tree (completely fictitious for demonstration purposes only).
Leaf
Node (class varaibleor prediction)
Classification CART v9d4

9. A simple example of a decision tree
Use height and weight to guess the sex of a person.
code 1 2 3 4
If Height > 180 cm Then Male
If Height <= 180 cm AND Weight > 80 kg Then Male
If Height <= 180 cm AND Weight <= 80 kg Then Female
Make Predictions With CART Models
The decision tree split this up into rectangles (when p=2 input variables) or some kind of hyper-rectangles with more inputs.
Testing to see if a person a male or not
Height > 180 cm: No
Weight > 80 kg: No
Therefore: Female
Classification CART v9d4

10. Classification CART v9d4
Exercise 1
Why it is a binary tree?
Answer: ____________________
How many nodes and leaves?
Answer: ________________
Male or Female if
183cm , 77 Kg? ANS:______
173 cm , 79 Kg? ANS: _____
177 cm , 85 Kg? ANS: ______
Classification CART v9d4

11. Classification CART v9d4
Exercise 1, ANSWER
Why it is a binary tree?
Answer: at each node it has 2 leaves
How many nodes and leaves?
Answer: Nodes:2, leaves 4.
Male or Female if
183cm , 77 Kg? ANS: Male
173 cm , 79 Kg? ANS: Female
177 cm , 85 Kg? ANS: Female
Classification CART v9d4

12. Classification CART v9d4
How to create a CART
Greedy Splitting : Grow the tree
Stopping Criterion: when the number of samples in a leaf is small enough.
Pruning The Tree: remove unnecessary leaves to
make it more efficient and
solve over fitting problems.
Classification CART v9d4

13. Classification CART v9d4
Greedy Splitting
During the process of growing the tree, you need to grow the leaves from a node by splitting.
You need a metric to evaluate your split is good or not, e.g. can use one of the followings:
Split using the attribute that the Gini (impurity) index is the lowest
Split using the attribute that the Information gain based on Entropy is the highest:
Information gain =Entropy(parent)-entropy(child)
Variance reduction
Classification CART v9d4

14. Classification CART v9d4
Example: data input
4 bus
3 bus
4 train
Classification CART v9d4

15. 1) Split metric : Entropy
Prob(bus) =4/10=0.4
Prob(car)=3/10=0.3
Prob(train)=3/10=0.3
Entropy=-0.4*log_2(0.4)- 0.3*log_2(0.3)-0.3*log_2(0.3)=1.571
(note:log_2 is log base 2.)
Another example: if P(bus)=1, P(car)=0, P(train)=0
Entropy = 1*log_2(1)-0*log_2( )- 0*log_2( )=0
Entropy = 0, it is very pure, Impurity is 0
Classification CART v9d4

16. Exercise 2 2) Split metric: Gini (impurity) index
Prob(bus) =4/10=0.4
Prob(car)=3/10=0.3
Prob(train)=3/10=0.3
Gini index =1-(0.4* * *0.3)= 0.66
Another example if the class has only bus: if P(bus)=1, P(car)=0, P(train)=0
Gini Impurity index= 1-1*1-0*0-0*0=0
Impurity is 0
Classification CART v9d4

17. Answer2 2) Split metric: Gini (impurity) index
Prob(bus) =4/10=0.4
Prob(car)=3/10=0.3
Prob(train)=3/10=0.3
Gini index =1-(0.4* * *0.3)= 0.66
Another example if the class has only bus: if P(bus)=1, P(car)=0, P(train)=0
Gini Impurity index= 1-1*1-0*0-0*0=0
Impurity is 0
Exercise
Classification CART v9d4

18. Classification CART v9d4
Exercise 3.
Train
If the first 2 rows are not bus but train, find entropy and Gini index
Prob(bus) =2/10=0.2
Prob(car)=3/10=0.3
Prob(train)=5/10=0.5
Entropy =_______________________________
Gini index =_____________________________
Train
Classification CART v9d4

19. Classification CART v9d4
ANSWER 3.
Train
If the first 2 rows are not bus but train, find entropy and Gini index
Prob(bus) =2/10=0.2
Prob(car)=3/10=0.3
Prob(train)=5/10=0.5
Entropy =-0.2*log_2(0.2)- 0.3*log_2(0.3)- 0.5log_2(0.5)= 1.485
Gini index =1-(0.2* * *0.5)= 0.62
Train
Classification CART v9d4

20. 3) Split metric : Classification error
Classification error=1-max(0.4,0.3,0.3)
=1-0.4=0.6
Another example: if P(bus)=1, P(car)=0, P(train)=0
Classification error=1-max(1,0,0)=0
Impurity is 0, if there is only bus
Classification CART v9d4

21. 4) Split metrics : Variance reduction
Introduced in CART,[3] variance reduction is often employed in cases where the target variable is continuous (regression tree), meaning that use of many other metrics would first require discretization before being applied. The variance reduction of a node N is defined as the total reduction of the variance of the target variable x due to the split at this node:
Classification CART v9d4

22. Splitting procedure: Recursive Partitioning for CART
Take all of your training data.
Consider all possible values of all variables.
Select the variable/value (X=t1) (e.g. X1=Height) that produces the greatest “separation” (or maximum homogeneity - - less impurity within each of the new part) in the target.
(X=t1) is called a “split”.
If X< t1 (e.g. Height <180cm) then send the data to the “left”; otherwise, send data point to the “right”.
Now repeat same process on these two “nodes”
You get a “tree”
Note: CART only uses binary splits.
Classification CART v9d4

23. Classification CART v9d4
Example 1
Classification CART v9d4

24. Classification CART v9d4
Example1
Day
Outlook
Temp.
Humidity
Wind
Decision 1
Sunny
Hot
High
Weak No 2
Strong 3
Overcast
Yes 4
Rain
Mild 5
Cool
Normal 6 7 8 9 10 11 12 13 14
Classification CART v9d4

25. Classification CART v9d4
Gini index
Gini index
Gini index is a metric for classification tasks in CART. It stores sum of squared probabilities of each class. We can formulate it as illustrated below.
Gini = 1 – Σ (Pi)2 for i=1 to number of classes
Classification CART v9d4

26. Classification CART v9d4
Outlook
Outlook is a nominal feature. It can be sunny, overcast or rain. I will summarize the final decisions for outlook feature.
Gini(Outlook=Sunny) = 1 – (2/5)2 – (3/5)2 = 1 – 0.16 – 0.36 = 0.48
Gini(Outlook=Overcast) = 1 – (4/4)2 – (0/4)2 = 0
Gini(Outlook=Rain) = 1 – (3/5)2 – (2/5)2 = 1 – 0.36 – 0.16 = 0.48
Then, we will calculate weighted sum of gini indexes for outlook feature.
Gini(Outlook) = (5/14) x (4/14) x 0 + (5/14) x 0.48 = = 0.342
Outlook
Yes No
Number of instances
Sunny 2 3 5
Overcast 4
Rain
Classification CART v9d4

27. Classification CART v9d4
Temperature
Similarly, temperature is a nominal feature and it could have 3 different values: Cool, Hot and Mild. Let’s summarize decisions for temperature feature.
Gini(Temp=Hot) = 1 – (2/4)2 – (2/4)2 = 0.5
Gini(Temp=Cool) = 1 – (3/4)2 – (1/4)2 = 1 – – = 0.375
Gini(Temp=Mild) = 1 – (4/6)2 – (2/6)2 = 1 – – = 0.445
We’ll calculate weighted sum of gini index for temperature feature
Gini(Temp) = (4/14) x (4/14) x (6/14) x = = 0.439
Humidity
Yes No
Number of instances
High 3 4 7
Normal 6 1
Classification CART v9d4

28. Classification CART v9d4
Humidity
Humidity is a binary class feature. It can be high or normal.
Gini(Humidity=High) = 1 – (3/7)2 – (4/7)2 = 1 – – = 0.489
Gini(Humidity=Normal) = 1 – (6/7)2 – (1/7)2 = 1 – – 0.02 = 0.244
Weighted sum for humidity feature will be calculated next
Gini(Humidity) = (7/14) x (7/14) x = 0.367
Humidity
Yes No
Number of instances
High 3 4 7
Normal 6 1
Classification CART v9d4

29. Classification CART v9d4
Wind
Wind is a binary class similar to humidity. It can be weak and strong. Gini(Wind=Weak) = 1 – (6/8)2 – (2/8)2 = 1 – – = Gini(Wind=Strong) = 1 – (3/6)2 – (3/6)2 = 1 – 0.25 – 0.25 = 0.5 Gini(Wind) = (8/14) x (6/14) x 0.5 = 0.428
Feature
Gini index
Outlook
0.342
Temperature
0.439
Humidity
0.367
Wind
0.428
Classification CART v9d4

30. Classification CART v9d4
Time to decide
We’ve calculated gini index values for each feature. The winner will be outlook feature because its cost is the lowest. We’ll put outlook decision at the top of the tree.
Feature
Gini index
Outlook
0.342
Temperature
0.439
Humidity
0.367
Wind
0.428
Classification CART v9d4

31. Classification CART v9d4
Time to decide
Put outlook decision at the top of the tree.
Classification CART v9d4

32. Classification CART v9d4
You might realize that sub dataset in the overcast leaf has only yes decisions. This means that overcast leaf is over.
Classification CART v9d4

33. Classification CART v9d4
We will apply same principles to those sub datasets in the following steps. Focus on the sub dataset for sunny outlook. We need to find the gini index scores for temperature, humidity and wind features respectively.
Day
Outlook
Temp.
Humidity
Wind
Decision 1
Sunny
Hot
High
Weak No 2
Strong 8
Mild 9
Cool
Normal
Yes 11
Classification CART v9d4

34. Gini of temperature for sunny outlook
Gini(Outlook=Sunny and Temp.=Hot) = 1 – (0/2)2 – (2/2)2 = 0
Gini(Outlook=Sunny and Temp.=Cool) = 1 – (1/1)2 – (0/1)2 = 0
Gini(Outlook=Sunny and Temp.=Mild) = 1 – (1/2)2 – (1/2)2 = 1 – 0.25 – 0.25 = 0.5
Gini(Outlook=Sunny and Temp.) = (2/5)x0 + (1/5)x0 + (2/5)x0.5 = 0.2
Temperature
Yes No
Number of instances
Hot 2
Cool 1
Mild
Classification CART v9d4

35. Gini of humidity for sunny outlook
Gini(Outlook=Sunny and Humidity=High) = 1 – (0/3)2 – (3/3)2 = 0
Gini(Outlook=Sunny and Humidity=Normal) = 1 – (2/2)2 – (0/2)2 = 0
Gini(Outlook=Sunny and Humidity) = (3/5)x0 + (2/5)x0 = 0
Humidity
Yes No
Number of instances
High 3
Normal 2
Classification CART v9d4

36. Gini of wind for sunny outlook
Gini(Outlook=Sunny and Wind=Weak) = 1 – (1/3)2 – (2/3)2 = 0.266
Gini(Outlook=Sunny and Wind=Strong) = 1- (1/2)2 – (1/2)2 = 0.2
Gini(Outlook=Sunny and Wind) = (3/5)x (2/5)x0.2 = 0.466
Wind
Yes No
Number of instances
Weak 1 2 3
Strong
Classification CART v9d4

37. Decision for sunny outlook
We’ve calculated gini index scores for feature when outlook is sunny. The winner is humidity because it has the lowest value.
We’ll put humidity check at the extension of sunny outlook
Feature
Gini index
Temperature
0.2
Humidity
Wind
0.466
Classification CART v9d4

38. Classification CART v9d4
Result d
Classification CART v9d4

39. Classification CART v9d4
As seen, decision is always no for high humidity and sunny outlook. On the other hand, decision will always be yes for normal humidity and sunny outlook. This branch is over.
Classification CART v9d4

40. Classification CART v9d4
Rain outlook
Now, we need to focus on rain outlook.
We’ll calculate gini index scores for temperature, humidity and wind features when outlook is rain.
Day
Outlook
Temp.
Humidity
Wind
Decision 4
Rain
Mild
High
Weak
Yes 5
Cool
Normal 6
Strong No 10 14
Classification CART v9d4

41. Gini of temperature for rain outlook
Gini(Outlook=Rain and Temp.=Cool) = 1 – (1/2)2 – (1/2)2 = 0.5
Gini(Outlook=Rain and Temp.=Mild) = 1 – (2/3)2 – (1/3)2 = 0.444
Gini(Outlook=Rain and Temp.) = (2/5)x0.5 + (3/5)x0.444 = 0.466
Temperature
Yes No
Number of instances
Cool 1 2
Mild 3
Classification CART v9d4

42. Gini of wind for rain outlook
Gini(Outlook=Rain Gini(Outlook=Rain and Wind=Weak) = 1 – (3/3)2 – (0/3)2 = 0
Gini(Outlook=Rain and Wind=Strong) = 1 – (0/2)2 – (2/2)2 = 0
Gini(Outlook=Rain and Wind) = (3/5)x0 + (2/5)x0 = 0
and Wind=Weak) = 1 – (3/3)2 – (0/3)2 = 0
Wind
Yes No
Number of instances
Weak 3
Strong 2
Classification CART v9d4

43. Decision for rain outlook
The winner is wind feature for rain outlook because it has the minimum gini index score in features.
Put the wind feature for rain outlook branch and monitor the new sub data sets.
Feature
Gini index
Temperature
0.466
Humidity
Wind
Classification CART v9d4

44. Classification CART v9d4
Put the wind feature for rain outlook branch and monitor the new sub data sets.
You might realize that sub dataset in the overcast leaf has only yes decisions. This means that overcast leaf is over.
Sub data sets for weak and strong wind and rain outlook
Classification CART v9d4

45. Classification CART v9d4
Final result
As seen, decision is always yes when wind is weak. On the other hand, decision is always no if wind is strong. This means that this branch is over.
Classification CART v9d4

46. Classification CART v9d4
Exercise
Repeat the previous example using the information gain method rather than the Gini index method
Classification CART v9d4

47. Classification CART v9d4
Example 2
Classification CART v9d4

48. An example: design a tree to find out whether an umbrella is needed
Weather Driving Class=Umbrella
1 Sunny Yes Yes
2 Cloudy No No
3 Rainy
The first question is :
Choose the root attribute:
You have two choices for the root attribute:
1) Weather
2) Driving
Classification CART v9d4

49. Classification CART v9d4
How to build the tree
First question: You have 2 choices
1) Root is attribute “Weather”: The braches are
Sunny or not , find metric M_sunny
Cloudy or not, find metric M_cloudy
Rainy or not, find metric M_rainy
Total weather_split_metric= weight_sunny*M_sunny+ weight_cloudy*M_cloudy+ weight_rainy*M_rainy
(If this is smaller, pick “weather” as root)
2) Root is attribute “Driving”:
Yes or n umbrella , find metric M_drive
Total split_metric_drive= weight_drive* M_drive
Note weight_drive =1, since it is the only choice
(If this is smaller, pick “driving” as root)
We will describe the procedure using 7 steps
Root=weather
Sunny
Cloudy
Rainy OR
Root=driving
Yes
(umbrella) No
(umbrella)
Classification CART v9d4

50. Steps to develop the tree. If root is attribute “weather”:
Sunny ?
step1
yes
Step1 : if root is attribute “weather”, branch is “Sunny”, find split metric (M_sunny)
Step2 : if root is attribute “weather”, branch is “Cloudy”, find split metric (M_cloudy)
Step3: if root is attribute “weather”, branch is “Rainy”, find split metric (M_rainy) No
Weather:
Cloudy ?
step2
yes No
Weather:
Rainy ?
step3
yes No
Classification CART v9d4 50

51. Step1: Find M_sunny, Weight_sunny
Weather:
Sunny ?
step1
yes
N=Number of samples=9
M1=Number of sunny cases=2
W1=Weight_sunny=M1/N=2/9
N1y=Num of Umbrella yes=0
N1n=Num of Umbrella No=2
Nsunny=2
G1=Gini=1- ((N1y/M1)^2+(N1n/M1)^2)
= 1-((0/2)^2+(2/2)^2)=0
Metric_sunny=G1 or E1 No
Classification CART v9d4

52. For step2: Find M_cloudy, Weight_cloudy
Weather:
Cloudy?
step2
yes No
N=Number of samples=9
M2=Number of cloudy cases=4
W2=Weight_cloudy=M2/N=4/9
N2y=Num of Umbrella Yes, when cloudy=2
N2n=Num of Umbrella No, when cloudy=2
G2=Gini=1- ((N2y/M2)^2+(N2n/M2)^2)
= 1-((2/4)^2+(2/4)^2)=0.5
Metric_cloudy=G2 or E2
Classification CART v9d4

53. For step3: Find M_rainy, Weight_rainy
Weather:
Rainy ?
step3
yes No
N=Number of samples=9
M3=Number of rainy cases=3
W3=Weight_rainy=M3/N=3/9
N3y=Num of Umbrella Yes, when rainy=1
N3n=Num of Umbrella No, when rainy=2
G3=Gini=1- ((N3y/M3)^2+(N3n/M3)^2)
= 1-((1/3)^2+(2/3)^2)=0.444
Metric_rainy=G3 or E3
Classification CART v9d4

54. Step4: metric for weather
weather_split_metric= weight_sunny*M_sunny+ weight_cloudy*M_cloudy+ weight_rainy*M_rainy
weather_split_metric_Gini= W1*G1+W2*G2+W3*G3
=(2/9)*0+(4/9)*0.5+(3/9)*0.44=
Classification CART v9d4

55. Step5: Find M_driving, Weight_driving
Weather:
Rainy ?
step5
yes No
N=Number of samples=9
M5=Number of driving cases=9
W5=Weight_rainy=M5/N=9/9
N5y=Num of Umbrella Yes, when driving=3
N5n=Num of Umbrella No, when driving=6
G5=Gini=1- ((N5y/M5)^2+(N5n/M5)^2)
= 1-((3/9)^2+(6/9)^2)=0.444
Metric_rainy=G3 or E3
Classification CART v9d4

56. Step6: metric for driving
driving_split_metric= driving_sunny*M_yes+ driving_cloudy*M_no
driving_split_metric_Gini= W5*G5=(9/9)*0.444= 0.444
Classification CART v9d4

57. Classification CART v9d4
Step7 make decision
Decide which is suitable to be the root (weather or driving)
Compare
weather_split_metric_Gini=
driving_split_metric_Gini= 0.444
Choose the lower score, so weather is selected as the root,
see more example
Can repeat this procedure for the development of leaves (subtrees)
Classification CART v9d4

58. Classification CART v9d4
To continue
Weather Driving Class=Umbrella
1 Sunny Yes Yes
2 Cloudy No No
3 Rainy
To continue the construction of the tree:
Should driving put under
sunny or
cloudy or
Rainy ?
Classification CART v9d4

59. Classification CART v9d4
Step8:
If weather=sunny, and the leaf =driving
Only 2 cases, so weight=2/9, and umbrella_yes=2, umbrella_no=0
Ginit_a=(2/9)*(1-(0/2)^2-(2/2)^2)=0
If weather=cloudy, and the leaf =driving
Only 4 cases, so weight=4/9, and umbrella_yes=2, umbrella_no=2
Ginit_b=(4/9)*(1-(2/4)^2-(2/4)^2)=0.222
If weather=rainy, and the leaf =driving
Only 3 cases, so weight=3/9, and umbrella_yes=1, umbrella_no=2
Ginit_c=(3/9)*(1-(1/3)^2-(2/3)^2)=0.148
Since Ginit_a is the smallest, so driving should be placed under sunny.
Can continue , similar to previous approaches, see blue solid lines and dotted lines (red)
Classification CART v9d4

60. Classification CART v9d4
The final result
Root=weather
Rainy
Sunny
cloudy
Driving
Driving
yes No
Driving
yes No No
umbrella
Yes
umbrella
yes No
umbrella
Not sure
Yes=2
N=1 for
umbrella
Sample is 3
Cannot resolve, but the sample is too small, so
we can ignore it No
umbrella
Classification CART v9d4

61. Classification CART v9d4
Student exercise
Temperature Humidity Weather Drive/walk Class=Umbrella
1 Low Low Sunny 1 Drive Yes
2 Medium Medium 2 Cloudy 2 Walk No
3 High High Rain
Classification CART v9d4

62. Exercise 4: which one (attribute) do we need to pick first??
Classification CART v9d4

63. Answer 4: which one (attribute) do we need to pick first??
Answer: determine the attribute that best classifies the training data; use this attribute at the root of the tree. Repeat this process at for each branch.
Classification CART v9d4

64. Classification CART v9d4
Overfitting
Problem and solution
Classification CART v9d4

65. Overfitting problem and solution
Problem: Your trained model only works for training data but will fail when handling new or unseen data
Solution: use validation set to prune (remove some leaves) your tree to avoid overfitting.
References:  
Classification CART v9d4

66. Classification CART v9d4
Pruning methods
Idea: Remove leaves that contribute little.
Pruning method: Cost-Complexity Pruning
The original Tree is T, it has a subtree T2, we prune T2 and the pruned tree is shown below
Tree T subtree T2 pruned tree
Classification CART v9d4

67. Classification CART v9d4
MATLAB DEMO
Classification CART v9d4

68. Classification CART v9d4
Defining terms
For the whole dataset : use about 70 % for training data; 30 % for testing (pruning and Cross-Validation use)
Choose examples for training/testing sets randomly
Training data is used to construct the decision tree (will be pruned)
Testing data is used for pruning
f=Error on training data
N= number if instances covered by the leaves
Z= Z score of a normal distribution
e=Error on testing data (calculated from f,N,z)
Classification CART v9d4

69. Post-pruning using Error estimation
Post-pruning using Error estimation
In the following example we set Z to 0.69 (see normal distribution curve) which is equal to a confidence level of 75%.
see
Classification CART v9d4

70. Post-pruning using cost-complexity
Classification CART v9d4

71. Use test set to find best prune result
Selected
Because RRC(T) is the smallest
RC=cross validation
Train set R
Test set R
Classification CART v9d4

72. Classification CART v9d4
Classification CART v9d4

73. Classification CART v9d4
Appendix
Classification CART v9d4

74. Classification CART v9d4
Example using sklearn
Using sklearn
from sklearn import tree
# You may hard code your data as given or to use a .csv file import csv then fetch your data from .csv file
# Assume we have two dimensional feature space with two classes we like distinguish
dataTable = [[2,9],[4,10],[5,7],[8,3],[9,1]]
dataLabels = ["Class A","Class A","Class B","Class B","Class B"]
# Declare our classifier
trained_classifier = tree.DecisionTreeClassifier()
# Train our classifier with data we have
trained_classifier = trained_classifier.fit(dataTable,dataLabels)
# We are done with training, so it is time to test it!
someDataOutOfTrainingSet = [[10,2]]
label = trained_classifier.predict(someDataOutOfTrainingSet)
# Show the prediction of trained classifier for data [11,2]
print(label[0])
Classification CART v9d4

75. Iris test using sklearn, this will generate dt.dot file
import numpy as np
from sklearn import datasets
from sklearn import tree
# Load iris
iris = datasets.load_iris()
X = iris.data
y = iris.target
# Build decision tree classifier
dt = tree.DecisionTreeClassifier(criterion='entropy')
dt.fit(X, y)
dotfile = open("dt.dot", 'w')
tree.export_graphviz(dt, out_file=dotfile, feature_names=iris.feature_names)
dotfile.close()
Classification CART v9d4

76. Classification CART v9d4
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
# Parameters
n_classes = 3
plot_colors = "ryb"
plot_step = 0.02
# Load data
iris = load_iris()
for pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3],
[1, 2], [1, 3], [2, 3]]):
# We only take the two corresponding features
X = iris.data[:, pair]
y = iris.target
# Train
clf = DecisionTreeClassifier().fit(X, y)
# Plot the decision boundary
plt.subplot(2, 3, pairidx + 1)
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),
np.arange(y_min, y_max, plot_step))
plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
cs = plt.contourf(xx, yy, Z, cmap=plt.cm.RdYlBu)
plt.xlabel(iris.feature_names[pair[0]])
plt.ylabel(iris.feature_names[pair[1]])
# Plot the training points
for i, color in zip(range(n_classes), plot_colors):
idx = np.where(y == i)
plt.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i],
cmap=plt.cm.RdYlBu, edgecolor='black', s=15)
plt.suptitle("Decision surface of a decision tree using paired features")
plt.legend(loc='lower right', borderpad=0, handletextpad=0)
plt.axis("tight")
plt.show()
Iris dataset
Classification CART v9d4

77. A working implementation in pure python
Classification CART v9d4

78. Classification CART v9d4
code
function tt4
clear
parent_en=entropy_cal([9,5])
%humidy
en1=entropy_cal([3,4])
en2=entropy_cal([6,1])
Information_gain(1)=parent_en-(7/14)*en1-(7/14)*en2
clear en1 en2
%outlook
en1=entropy_cal([3,2])
en2=entropy_cal([4,0])
en3=entropy_cal([2,3])
Information_gain(2)=parent_en-(5/14)*en1-(4/14)*en2-(5/14)*en3
clear en1 en2 en3
%wind
en1=entropy_cal([6,2])
en2=entropy_cal([3,3])
Information_gain(3)=parent_en-(8/14)*en1-(6/14)*en2
%temperature
en1=entropy_cal([2,2]) %hot 2 yes , 2 no
en2=entropy_cal([3,1]) %mild 3 yes, 1 no
en3=entropy_cal([4,2]) %cool 4 yes, 2 no
Information_gain(4)=parent_en-(4/14)*en1-(4/14)*en2-(6/14)*en3
Information_gain
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [en]=entropy_cal(e)
n=length(e);
base=sum(e);
%% probabilty of the elements in the input
for i=1:n
p(i)=e(i)/base;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
temp=0;
if p(i)==0 %to avoid the problem of -inf
else
temp= p(i)*log2(p(i))+temp;
en=-temp;
Classification CART v9d4

79. A tree showing nodes, branches, leaves , attributes and target classes?
Root node
If attribute X=Raining
Branch: No
Branch: yes ? ?
Leaf node1
If attribute X=sunny
Leaf node3
If attribute Z=driving ?
Branch: No
Branch: Yes No
Yes
Leaf node2
If Y=stay outdoor
Target
Class= umbrella
Target
Class= No umbrella
Target
Class= No umbrella
Branch: Yes
Branch: No
Target
Class= umbrella
Target
Class= No umbrella
Classification CART v9d4

80. Classification CART v9d4
reference
Classification CART v9d4