As discussed in the previous update, I have applied the decision tree to my project.
The code goes like this:
The output that we got is:
In our decision tree analysis, we focused on predicting individual behaviors in varied incident scenarios. These ranged from situations where individuals didn’t flee to those where they fled by car, on foot, or through other means. Our model achieved an accuracy of approximately 67%, reflecting a substantial ability to predict correctly in a majority of the cases. This level of accuracy underscores the model’s general effectiveness in assessing and interpreting varied behavioral responses during different incident scenarios.
However, a closer inspection of the confusion matrix revealed certain misclassifications. While the model successfully identified 37 instances where individuals did not flee and accurately predicted 676 fleeing incidents, it also misclassified several cases. Notably, there were 125 instances where the model inaccurately predicted fleeing when it didn’t occur, 136 cases were wrongly classified as fleeing by car, and 33 cases were incorrectly predicted as fleeing on foot. These misclassifications point to specific areas in the model that require refinement to enhance its predictive accuracy and reliability.