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Everything About Decision Tree From Scratch

By Admin Published in Machine Learning 8-10 mins
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Decision tree

The decision tree is the classification algorithm in ML(Machine Learning). A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements.

To understand the algorithm of the decision tree we need to know about the classification.

What is Classification?

Classification is the process of dividing the datasets into different categories or groups by adding a label. It adds the data point to a particular labeled group on the basis of some condition.

As we see in daily life there are three categories in an email(Spam, Promotions, Personal) they are classified to get the proper information. Here decision tree is used to classify the mail type and fix it the proper one.

Types of classification

  • DECISION TREE
  • RANDOM FOREST
  • NAIVE BAYES
  • KNN

Decision tree:

  1. Graphical representation of all the possible solutions to a decision.
  2. A decision is based on some conditions.
  3. The decision made can be easily explained.

There are the following steps to get a decision with the decision tree

  1. Entropy:

Entropy is basically used to create a tree. We find our entropy from attribute or class. A decision tree is built top-down from a root node and involves partitioning the data into subsets that contain instances with similar values (homogeneous). ID3 algorithm uses entropy to calculate the homogeneity of a sample.

2.Information Gain:

The information gain is based on the decrease in entropy after a data-set is split on an attribute. Constructing a decision tree is all about finding an attribute that returns the highest information gain.

  • The information gain is based on the decrease in entropy after a dataset is split on an attribute.
  • Constructing a decision tree is all about finding an attribute that returns the highest information gain (i.e., the most homogeneous branches).
  • Gain(S, A) = Entropy(S) – ∑ [ p(S|A) . Entropy(S|A) ]
  • We intend to choose the attribute, splitting by which information gain will be the most
  • Next step is calculating information gain for all attributes

Here the short example of a Decision tree:

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

%matplotlib inline

play_data=pd.read_csv('data/tennis.csv.txt')

print(play_data)

play_data=pd.read_csv('data/tennis.csv.txt')

play_data

Output:

outlook temp humidity windy play

0 sunny hot high False no

1 sunny hot high True no

2 overcast hot high False yes

3 rainy mild high False yes

4 rainy cool normal False yes

5 rainy cool normal True no

6 overcast cool normal True yes

7 sunny mild high False no

8 sunny cool normal False yes

9 rainy mild normal False yes

10 sunny mild normal True yes

11 overcast mild high True yes

12 overcast hot normal False yes

13 rainy mild high True no

Entropy of play:

  • Entropy(play) = – p(Yes) . log2p(Yes) – p(No) . log2p(No)

play_data.play.value_counts()

Entropy_play=-(9/14)*np.log2(9/14)-(5/14)*np.log2(5/14)

print(Entropy_play)

output:

0.94028595867063114

Information Gain on splitting by Outlook

  • Gain(Play, Outlook) = Entropy(Play) – ∑ [ p(Play|Outlook) . Entropy(Play|Outlook) ]
  • Gain(Play, Outlook) = Entropy(Play) – [ p(Play|Outlook=Sunny) . Entropy(Play|Outlook=Sunny) ] – [ p(Play|Outlook=Overcast) . Entropy(Play|Outlook=Overcast) ] – [ p(Play|Outlook=Rain) . Entropy(Play|Outlook=Rain) ]

play_data[play_data.outlook == 'sunny']

Entropy(Play|Outlook=Sunny)

Entropy_Play_Outlook_Sunny =-(3/5)*np.log2(3/5) -(2/5)*np.log2(2/5)

Entropy_Play_Outlook_Sunny

play_data[play_data.outlook == 'overcast'] # Entropy(Play|Outlook=overcast)

Since, it's a homogenous data entropy will be 0

play_data[play_data.outlook == 'rainy'] # Entropy(Play|Outlook=rainy)

Entropy_Play_Outlook_Rain = -(2/5)*np.log2(2/5) - (3/5)*np.log2(3/5)

print(Entropy_play_Outlook_Rain)

Entropy(Play_Sunny|)

Entropy_Play_Outlook_Sunny =-(3/5)*np.log2(3/5) -(2/5)*np.log2(2/5)

#Gain(Play, Outlook) = Entropy(Play) – [ p(Play|Outlook=Sunny) . Entropy(Play|Outlook=Sunny) ] –

#[ p(Play|Outlook=Overcast) . Entropy(Play|Outlook=Overcast) ] – [ p(Play|Outlook=Rain) . Entropy(Play|Outlook=Rain) ]

Other gains

  • Gain(Play, Temperature) – 0.029
  • Gain(Play, Humidity) – 0.151
  • Gain(Play, Wind) – 0.048

Conclusion – Outlook is winner & thus becomes root of the tree

Time to find the next splitting criteria¶

play_data[play_data.outlook == 'overcast'] play_data[play_data.outlook == 'sunny'] # Entropy(Play_Sunny|)

Entropy_Play_Outlook_Sunny =-(3/5)*np.log2(3/5) -(2/5)*np.log2(2/5)

print(Entropy_Play_Outlook_Sunny)

Entropy(Play_Sunny|)

Entropy_Play_Outlook_Sunny =-(3/5)*np.log2(3/5) -(2/5)*np.log2(2/5)

print(Entropy_Play_Outlook_Sunny)

Information Gain for humidity

#Entropy for attribute high = 0, also entropy for attribute normal = 0

Entropy_Play_Outlook_Sunny - (3/5)*0 - (2/5)*0

Information Gain for windy

  • False -> 3 -> [1+ 2-]
  • True -> 2 -> [1+ 1-]

Entropy_Wind_False = -(1/3)*np.log2(1/3) - (2/3)*np.log2(2/3)

print(Entropy_Wind_False)

Entropy_Play_Outlook_Sunny - (3/5)* Entropy_Wind_False - (2/5)*1

Information Gain for temperature

  • hot -> 2 -> [2- 0+]
  • mild -> 2 -> [1+ 1-]
  • cool -> 1 -> [1+ 0-]

Entropy_Play_Outlook_Sunny - (2/5)*0 - (1/5)0 - (2/5) 1]

Conclusion : Humidity is the best choice on sunny branch:

play_data[(play_data.outlook == 'sunny') & (play_data.humidity == 'high')]

Output:

outlook temp humidity windy play

0 sunny hot high False no

1 sunny hot high True no

7 sunny mild high False no

play_data[(play_data.outlook == 'sunny') & (play_data.humidity == 'normal']

Output:

outlook temp humidity windy play

8 sunny cool normal False yes

10 sunny mild normal True yes

Splitting the rainy branch:

play_data[play_data.outlook == 'rainy'] # Entropy(Play_Rainy|)

Entropy_Play_Outlook_Rainy =-(3/5)*np.log2(3/5) -(2/5)*np.log2(2/5)outlook temp humidity windy play

3 rainy mild high False yes

4 rainy cool normal False yes

5 rainy cool normal True no

9 rainy mild normal False yes

13 rainy mild high True no

Information Gain for temp

  • mild -> 3 [2+ 1-]
  • cool -> 2 [1+ 1-]

Entropy_Play_Outlook_Rainy - (3/5)*0.918 - (2/5)*1

Output:

0.020150594454668602

Information Gain for Windy:

Entropy_Play_Outlook_Rainy - (2/5)*0 - (3/5)*0

Output:

0.97095059445466858

Information Gain for Humidity

  • High -> 2 -> [1+ 1-]
  • Normal -> 3 -> [2+ 1-]

Entropy_Play_Outlook_Rainy_Normal = -(1/3)*np.log2(1/3) - (2/3)*np.log2(2/3)

Entropy_Play_Outlook_Rainy_Normal

Entropy_Play_Outlook_Rainy - (2/5)*1 - (3/5)*Entropy_Play_Outlook_Rainy_Normal

Entropy_Play_Outlook_Rainy_Normal

Entropy_Play_Outlook_Rainy_Normal

Output:

0.91829583405448956

0.019973094021974891

Final tree:

Decision trees are popular among non-statisticians as they produce a model that is very easy to interpret. Each leaf node is presented as an if/then rule. Cases that satisfy the if/then the statement is placed in the node. Are non-parametric and therefore do not require normality assumptions of the data. Parametric models specify the form of the relationship between predictors and response. An example is a linear relationship for regression. In many cases, however, the nature of the relationship is unknown. This is a case in which non-parametric models are useful. Can handle data of different types, including continuous, categorical, ordinal, and binary. Transformations of the data are not required. It can be useful for detecting important variables, interactions, and identifying outliers. It handles missing data by identifying surrogate splits in the modeling process. Surrogate splits are splitting highly associated with the primary split. In other models, records with missing values are omitted by default.

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