A binary variable was developed to denote instances where the service time exceeds 30 minutes. This variable serves as the target for the predictive model, which aims to ascertain if the service time will surpass this 30-minute threshold in the test dataset. The model’s predictive capability is then quantitatively assessed by measuring its accuracy, which reflects the proportion of total predictions that were correct.
In addition to accuracy, a confusion matrix was generated. This matrix is a critical tool in evaluating the performance of the model in binary classification tasks. It presents a detailed breakdown of the model’s predictions, showcasing not only the correct predictions (true positives and true negatives) but also the errors it made (false positives and false negatives). This comprehensive analysis allows for a deeper understanding of the model’s strengths and weaknesses, particularly in differentiating between instances with service times above and below the 30-minute mark. By combining the accuracy metric with the insights from the confusion matrix, a more nuanced evaluation of the model’s effectiveness in binary classification is achieved.