Performance evaluation of random forest algorithm for automating classification of mathematics question items

Automated classification of mathematics question items based on the Table Specifications is crucial in developing well-defined assessment content, significantly reducing teachers’ workload. This study presents a performance evaluation Random Forest model designed to classify content standards first quarter tenth grade stipulated by Philippines’ Department Education Curriculum Guide. The uses an algorithm that extracts mathematical expressions as tokens for Bag-of-words Model. was conducted using precision, recall, F-1 score, and overall accuracy metrics, confusion matrix used assess model’s performance. results showed achieved 95% accuracy, demonstrating its effectiveness classifying items.