Two datasets (Golf, Car) can be found on Piazza. In each dataset, each row corresponds to a record. The last column corresponds to the class label, and the remaining columns are the attributes. README presents the meanings of the attributes in these two datasets.
In this assignment, you are asked to implement Decision Tree algorithm. Template (decisionTree_template.py) is for Python 3. In the template, you are asked to fill in two functions: chooseBestFeature and stopCriteria. In chooseBestFeature, you need to use Gini index as the impurity measure to decide the best feature to split. In stopCriteria, you need to check whether the stopping criteria are satisfied, and return the class label assigned to the leaf node.
The attributes in the provided datasets are either nominal or ordinal. You need to consider multi-way split strategy to split the attributes in this assignment.
Do not directly call a function or package that implements Decision Tree algorithm. You need to implement the algorithm by yourself. If you are not sure about whether it is OK to use a certain function, please post your question on Piazza.
Please take the following steps:
Implement Decision Tree algorithm as follows:
DTree(records, attributes) returns a tree
If stopping criterion is met, return a leaf node with the assigned class.
Else pick an attribute F based on Gini Index and create a node R for it For each possible value v of F:
Let Sv be the subset of records that have value v for F call DTree(Sv, attributes – {F}) and attach the resulting tree as the subtree to the current node.
Return the subtree.
Test your Decision Tree algorithm on Golf dataset. Based on your output, you can use the provided py file to automatically draw the tree. Using multi-way split, the resulting tree for the Golf dataset should look like:
To draw the tree, you can use the provided treeplot.py file, or just draw the tree using Excel or PowerPoint, or draw the tree on a piece of paper and include a scanned copy of the tree in the report.
If you get the correct tree, then apply your algorithm on the Car dataset and draw the tree.
