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What is Pulsar Classification with Random Forest about?

This project replicates and enhances a supervised learning framework for classifying pulsar candidates using the HTRU2 dataset, achieving a Random Forest model performance of 91.16% recall and 96.90% accuracy. The study addresses the challenge of detecting genuine pulsar signals in an imbalanced dataset, where pulsars represent less than 10% of observations. Key methodologies included threshold optimization and data balancing, demonstrating the model

Author

Adishree Gupta

Language

EN