Decision Tree Algorithm | Decision Tree in Python | Machine Learning Algorithms | Edureka

Copyright © 2018, edureka and/or its affiliates. All rights reserved.Machine Leaning Training Using Python
Agenda for Today’s Session
▪ What is Classification?
▪ Types of Classification
▪ Classification Use case
▪ What is Decision Tree?
▪ Terminologies associated to a Decision Tree
▪ Visualizing a Decision Tree
▪ Writing a Decision Tree Classifier form Scratch in Python using
CART Algorithm

Copyright © 2018, edureka and/or its affiliates. All rights reserved.
What is Classification?
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“Classification is the process of dividing the datasets
into different categories or groups by adding label”
What is
Classification?
▪ Note: It adds the data point to a particular
labelled group on the basis of some condition”

Types of
Classification
Decision Tree
Random Forest
Naïve Bayes
KNN
Decision Tree
▪ Graphical representation of all the possible solutions to a decision
▪ Decisions are based on some conditions
▪ Decision made can be easily explained

Types of
Classification
Decision Tree
Random Forest
Naïve Bayes
KNN
Random Forest
▪ Builds multiple decision trees and merges them together
▪ More accurate and stable prediction
▪ Random decision forests correct for decision trees' habit
of overfitting to their training set
▪ Trained with the “bagging” method

Types of
Classification
Decision Tree
Random Forest
Naïve Bayes
KNN
Naïve Bayes
▪ Classification technique based on Bayes' Theorem
▪ Assumes that the presence of a particular feature in a class is
unrelated to the presence of any other feature

Types of
Classification
Decision Tree
Random Forest
Naïve Bayes
KNN
K-Nearest Neighbors
▪ Stores all the available cases and classifies new cases
based on a similarity measure
▪ The “K” is KNN algorithm is the nearest neighbors we wish
to take vote from.

What is Decision Tree?
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“A decision tree is a graphical representation of all
the possible solutions to a decision based on certain
conditions”
What is
Decision Tree?

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Understanding a Decision Tree

Colour Diameter Label
Green 3 Mango
Yellow 3 Mango
Red 1 Grape
Red 1 Grape
Yellow 3 Lemon
Dataset
This is how our dataset looks like!

`
Decision
Tree
is diameter > = 3?
Color Diam Label
Green 3 Mango
Yellow 3 Lemon
Red 1 Grape
Yellow 3 Mango
Red 1 Grape
G 3 Mango
Y 3 Mango
Y 3 Lemon
R 1 Grape
R 1 Grape
is colour = = Yellow?
Y 3 Mango
Y 3 Lemon
G 3 Mango
Gini Impurity = 0.44
Gini Impurity = 0
Information Gain = 0.37
Information
Gain = 0.11
100% Grape
100% Mango
50% Mango
50% Lemon

Is the colour green?
Is the diameter >=3
Is the colour yellow
TRUE False
Green 3 Mango
Yellow 3 Lemon
Yellow 3 Mango
`
What is
Decision Tree?

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Decision Tree Terminologies

Decision Tree Terminology
Pruning
Opposite of Splitting, basically
removing unwanted branches from
the tree
Root Node
It represents the entire population or
sample and this further gets divided
into two or more homogenous sets.
Parent/Child Node
Root node is the parent node and all
the other nodes branched from it is
known as child node
Splitting
Splitting is dividing the root node/sub
node into different parts on the basis
of some condition.
Leaf Node
Node cannot be further segregated
into further nodes
Branch/SubTree
Formed by splitting the tree/node

is diameter > = 3?
Color Diam Label
Green 3 Mango
Yellow 3 Lemon
Red 1 Grape
Yellow 3 Mango
Red 1 Grape
is colour = = Yellow?
G 3 Mango
Y 3 Mango
Y 3 Lemon
R 1 Grape
R 1 Grape
100% Grape Y 3 Mango
Y 3 Lemon
100% Mango
50% Mango
50% Lemon

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CART Algorithm

Let’s First Visualize the Decision Tree
Which Question to ask and When?

Let’s First Visualize the Decision Tree
No
Yes
NormalHigh
Yes
WeakStrong
No Yes
Outlook
WindyHumidity

Learn about Decision Tree
Which one among them
should you pick first?

Answer: Determine the
attribute that best
classifies the training data

But How do we choose
the best attribute?
Or
How does a tree decide
where to split?

How Does A Tree Decide Where To Split?
Information Gain
The information gain is the decrease in
entropy after a dataset is split on the basis
of an attribute. Constructing a decision tree
is all about finding attribute that returns the
highest information gain
Gini Index
The measure of impurity (or purity) used in
building decision tree in CART is Gini Index
Reduction in Variance
Reduction in variance is an algorithm used
for continuous target variables (regression
problems). The split with lower variance is
selected as the criteria to split the
population
Chi Square
It is an algorithm to find out the statistical
significance between the differences
between sub-nodes and parent node

Let’s First Understand What is Impurity
Impurity = 0

Let’s First Understand What is Impurity
Impurity ≠ 0

What is
Entropy?
▪ Defines randomness in the data
▪ Entropy is just a metric which measures the impurity or
▪ The first step to solve the problem of a decision tree

What is
Entropy?
If number of yes = number of no ie P(S) = 0.5
 Entropy(s) = 1
If it contains all yes or all no ie P(S) = 1 or 0
 Entropy(s) = 0
- P(yes) log2 P(yes) − P(no) log2 P(no)Entropy(s) =
Where,
▪ S is the total sample space,
▪ P(yes) is probability of yes

What is
Entropy?
E(S) = -P(Yes) log2 𝑃(𝑌𝑒𝑠)
When P(Yes) =P(No) = 0.5 ie YES + NO = Total Sample(S)
E(S) = 0.5 log2 0.5 − 0.5 log2 0.5
E(S) = 0.5( log2 0.5 - log2 0.5)
E(S) = 1

What is
Entropy?
E(S) = -P(Yes) log2 𝑃(𝑌𝑒𝑠)
When P(Yes) = 1 ie YES = Total Sample(S)
E(S) = 1 log2 1
E(S) = 0
E(S) = -P(No) log2 𝑃(𝑁𝑜)
When P(No) = 1 ie No = Total Sample(S)
E(S) = 1 log2 1
E(S) = 0

What is
Information
Gain?
▪ Measures the reduction in entropy
▪ Decides which attribute should be selected as the
decision node
If S is our total collection,
Information Gain = Entropy(S) – [(Weighted Avg) x Entropy(each feature)]

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Let’s Build Our Decision Tree

Out of 14 instances we have 9 YES and 5 NO
So we have the formula,
E(S) = -P(Yes) log2 𝑃(𝑌𝑒𝑠) − P(No) log2 𝑃(𝑁𝑜)
E(S) = - (9/14)* log2 9/14 - (5/14)* log2 5/14
E(S) = 0.41+0.53 = 0.94
Step 1: Compute the entropy for the Data set
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
D11
D12
D13
D14

Which Node To Select As Root Node?
Outlook? Temperature?
Humidity? Windy?

Which Node To Select As Root Node: Outlook
Outlook?
Sunny Overcast
Yes
Yes
No
No
No
Yes
Yes
Yes
Yes
Rainy
Yes
Yes
Yes
No
No

E(Outlook = Sunny) = -2/5 log2 2/5 − 3/5 log2 3/5 = 0.971
E(Outlook = Overcast) = -1 log2 1
E(Outlook = Sunny) = -3/5 log2 3/5
− 0 log2 0 = 0
− 2/5 log2 2/5 = 0.971
I(Outlook) = 5/14 x 0.971 + 4/14 x 0 + 5/14 x 0.971 = 0.693
Information from outlook,
Information gained from outlook,
Gain(Outlook) = E(S) – I(Outlook)
0.94 – 0.693 = 0.247

Windy?
False
Yes
Yes
Yes
Yes
Yes
Yes
No
No
True
Yes
Yes
Yes
No
No
No

Which Node To Select As Root Node: Windy
E(Windy = True) = 1
E(Windy = False) = 0.811
I(Windy) = 8/14 x 0.811+ 6/14 x 1 = 0.892
Information from windy,
Information gained from outlook,
Gain(Windy) = E(S) – I(Windy)
0.94 – 0.892 = 0.048

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Similarly We Calculated For Rest Two

Which Node To Select As Root Node
Outlook:
Info 0.693
Gain: 0.940-0.693 0.247
Temperature:
Info 0.911
Gain: 0.940-0.911 0.029
Windy:
Info 0.892
Gain: 0.940-0.982 0.048
Humidity:
Info 0.788
Gain: 0.940-0.788 0.152
Since Max gain = 0.247,
Outlook is our ROOT Node

Which Node To Select Further?
Outlook
Yes ??
Overcast
Outlook = overcast
Contains only yes
You need to
recalculate things

This Is How Your Complete Tree Will Look Like
No
Yes
NormalHigh
Yes
WeakStrong
No Yes
Outlook
WindyHumidity
Overcast

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What Should I Do To Play - Pruning

“A decision tree is a graphical representation of all
the possible solutions to a decision based on certain
conditions”
What is
Pruning?

Pruning: Reducing The Complexity
Yes
Normal
Yes
Weak
Yes
Outlook
WindyHumidity

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Are tree based models better than
linear models?

Decision Tree Algorithm | Decision Tree in Python | Machine Learning Algorithms | Edureka

Decision Tree Algorithm | Decision Tree in Python | Machine Learning Algorithms | Edureka

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