Flattening the Recall Line Using a Voting Classifier for Forest Cover Type Data

To address the challenge of flattening the recall line in Forest Cover Type data classification, this study focuses on the application of a Voting Classifier. Forest cover is crucial for biodiversity preservation and climate regulation, and accurate classification of forest cover types is essential for effective forest management. The paper utilizes a dataset containing attributes related to forest cover, and models such as K-Nearest Neighbors (KNN), Extra Tree, Random Forest (RF), and Extreme Gradient Boosting (XGBoost) are employed. However, the individual performance of the models varies for recall. To overcome this, a Voting Classifier is introduced, which combines the predictions from multiple models using a majority or weighted vote. The experiments demonstrate the effectiveness of the Voting Classifier in flattening the recall line and enhancing the accuracy of forest cover type classification.