A Decision Tree Classifier model implemented in python. The sklearn.tree library is used to import the DecisionTreeClassifier class. The object of the class is created and passed the following arguments:
- criterion = 'entropy'
- random_state = 0
All other arguments used in the classifier object are default values provied by the class.
The classifier is fitted with the X_Train and Y_Train dataset. The confusion matrix is created to test the accuracy of the model. A total of 9 incorrect prediction was made by the Decision Tree Classifier model.