Description

MACHINE INTELLIGENCE LABORATORY- PESU UE19CS305
Teaching Assistantsvighneshkamath43@gmail.com sarasharish2000@gmail.com
Decision Trees are one of the easiest and popular classification algorithms to understand and interpret. The goal of using a Decision Tree is to create a training model that can be used to predict the class or value of the target variable by learning simple decision rules inferred from prior data.
The primary challenge in the decision tree implementation is to identify which attributes to consider as the root node at each level. Handling this is known as attribute selection. The ID3 algorithm builds decision trees using a top-down greedy search approach through the space of possible branches with no backtracking. It always makes the choice that seems to be the best at that moment.
Attribute selection in the ID3 algorithm involves various steps such as computing entropy, information gain and selecting the most appropriate attribute as the root node.
In this assignment you are are required to prepare a module that will help any machine learning fresher to use to calculate these heuristic on any categorical attributed data
Your task is to complete the code for the function whose skeleton is predefined.
You are provided with the following files:
1. week3.py
2. SampleTest.py
Note: These sample test cases are just for your reference.
SAMPLE TEST CASE Data

Note: This is the same dataset that was used in class for ID3 Algorithm.
Important Points:
1. Please do not make changes to the function definitions that are provided to you. Use the skeleton as it has been given. Also do not make changes to the sample test file provided to you. Run it as it is.
2. You are free to write any helper functions that can be called in any of these predefined functions given to you.
3. Your code will be auto evaluated by our testing script and our dataset and test cases will not be revealed. Please ensure you take care of all edge cases!
5. Point number 4 stresses on the fact that you should not hard code anything. Remember you are designing a module to help for ID3 algorithm of any kind of data.
6. The dataset we will be testing against will have N columns with N-1 attributes and Nth column being the target value.
7. Ensure you follow convention while returning from these functions.
10. Hidden test cases will not be revealed post evaluation.
week3 .py
Contains four function
1. get_entropy_of_dataset
2. get_avg_info_of_attribute
3. get_information_gain
4. get_selected_attribute
Function Name Input Parameter Return Value
get_entropy_of_dataset df : pandas dataframe of the given dataset entropy : Entropy of the entire
dataset (int/float)
get_avg_info_of_attribute df : pandas dataframe of the
given dataset attribute: name of the attribute (column name) whose Avg info is to be found Ex: “Temperature” Avg_info : Average Information
of that attribute (int/float)
get_information_gain df : pandas dataframe of the
given dataset
attribute: name of the attribute (column name) whose info gain is to be found Ex: “Temperature” Information_gain : Information gain of that attribute (int/float)
get_selected_attribute df : pandas dataframe of the
given dataset tuple((information_gains, selected_column)
Information_gains: python dictionary with key as column name and value as its information gain
Selected column : string,basically the selected column name for split
Example:
({‘A’:0.123,’B’:0.768,’C’:1.23} ,
‘C’)
2. You can import libraries that come built-in with python 3.7
3. You cannot change the skeleton of the code
SampleTest.py
1. This will help you check your code.
2. Note that the test case used in this is same as the graph defined above
3. Passing the cases in this does not ensure full marks ,you will need to take care of edge cases
4. Name your code file as YOUR_SRN.py
5. Run the command python3 SampleTest.py –SRN YOUR_SRN (incase of any import error use the below command)
python3.7 SampleTest.py –SRN YOUR_SRN
You are required to complete code in week2.py and rename it to SRN.py
Failing to submit in the right format will lead to zero marks without a chance for correction
Example: If your SRN is PES1201801819 your file should be PES1201801819.py
If your SRN is PES1UG19CS020 your file should be PES1UG19CS020.py
Use your SRN and not USN
All the helper function should be in the same file, make sure you run the sample test case before submitting.
Where to submit: Edmodo