Classification of Fire and Smoke Images using Decision Tree Algorithm in Comparison with Logistic Regression to Measure Accuracy, Precision, Recall, F-score

The study's objective is to assess how well the decision tree algorithm and the logistic regression algorithm classify photographs of fire and smoke. A total of 4,000 photos are captured, of which 2,000 are of fire and the remaining 2,000 are of smoke. A training dataset (n=3200, or 80% of the dataset) and a validation dataset (n=800, or 20% of the dataset) are each created. Classification is carried out using Python's Sklearn machine learning package. Metrics like precision, recall, f score, and accuracy numbers are used to assess an algorithm's performance. In the classification of photos of fire and smoke, the Novel Decision Tree provides 90.54 percent accuracy whereas logistic regression provides 85.75 percent accuracy (p 0.001). Compared to the logistic regression algorithm, the decision tree algorithm performed noticeably better.