< Overview >
The Random Forest and XGBoost model has been used in this project to train datasets in both CPU and GPU to classify Higgs boson signal from the background noise.
- Dataset contains almost 11 million simulated collison occurrences which was generated using Monte Carlo simulations.
- Both models were trained on the CPU and GPU (used RAPIDS on Nvidia RTX 3090) to determine how well each model performs in different environments
- In the RF and XGBoost models, the GPU is over 177 times and 300 times quicker than the CPU, respectively, allowing for significant time savings that can be leveraged to improve the model
- In terms of accuracy, the CPU performed slightly better than the GPU in both models when the same parameter was utilized