GitHub - Mital188/Decision-Tree: implements ID3 algorithm which would calculate the entropy and information gain and based on these values, the attributes are selected. These acquired information is used to create the decision tree. The entropy and hence the information gain is calculated using the training data. Pruning is carried out using the validation data.

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implements ID3 algorithm which would calculate the entropy and information gain and based on these values, the attributes are selected. These acquired information is used to create the decision tree. The entropy and hence the information gain is calculated using the training data. Pruning is carried out using the validation data.

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Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
.idea		.idea
data_sets1		data_sets1
data_sets2		data_sets2
Assignment.docx		Assignment.docx
Decision_tree.java		Decision_tree.java
Main_project.java		Main_project.java
README.txt		README.txt
Tree_pruning.java		Tree_pruning.java

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This assignment is implemented using JAVA.

Project Structure:
------------------
Decision Tree ID3_program
    ---> Decision_tree.py
    ---> Tree_pruning.py
    ---> Main_program.py
    ---> data_sets1/test_set.csv
    ---> data_sets1/training_set.csv
    ---> data_sets1/validation_set.csv
    ---> data_sets2/test_set.csv
    ---> data_sets2/training_set.csv
    ---> data_sets2/validation_set.csv

To run the code, use the following command:
-------------------------------------------
--> javac Main_program.java
--> java Main_program <Lvalue><Kvalue><training set file> <validation set file> <Test set file> <"yes" or "no">
Example:
--------
  javac Main_program.java
  java Main_program.java 20 5 “training_set.csv” “validation_set.csv” “test_set.csv” “yes”