Hyperparameter tuning is a key step in developing high-performing machine learning models, but searching large hyperparameter spaces requires extensive computation using standard sequential methods. This work analyzes...Hyperparameter tuning is a key step in developing high-performing machine learning models, but searching large hyperparameter spaces requires extensive computation using standard sequential methods. This work analyzes the performance gains from parallel versus sequential hyperparameter optimization. Using scikit-learn’s Randomized SearchCV, this project tuned a Random Forest classifier for fake news detection via randomized grid search. Setting n_jobs to -1 enabled full parallelization across CPU cores. Results show the parallel implementation achieved over 5× faster CPU times and 3× faster total run times compared to sequential tuning. However, test accuracy slightly dropped from 99.26% sequentially to 99.15% with parallelism, indicating a trade-off between evaluation efficiency and model performance. Still, the significant computational gains allow more extensive hyperparameter exploration within reasonable timeframes, outweighing the small accuracy decrease. Further analysis could better quantify this trade-off across different models, tuning techniques, tasks, and hardware.展开更多
Parallel Knowledge Base Machine PKBM95 is a kind of special computer which is designed to improve the inference capability of production systems. Its hardware architecture is a multiprocessor, consisting of one microc...Parallel Knowledge Base Machine PKBM95 is a kind of special computer which is designed to improve the inference capability of production systems. Its hardware architecture is a multiprocessor, consisting of one microcomputer and four TRANSPUTERs. We will focus our discussion on the concentration-scattered inference model and the twice-conflict resolution strategy presented in the this paper, as well as the architecture and operating language of PKBM95. According to experiments, they are effective in improving the inference capability of the system.展开更多
文摘Hyperparameter tuning is a key step in developing high-performing machine learning models, but searching large hyperparameter spaces requires extensive computation using standard sequential methods. This work analyzes the performance gains from parallel versus sequential hyperparameter optimization. Using scikit-learn’s Randomized SearchCV, this project tuned a Random Forest classifier for fake news detection via randomized grid search. Setting n_jobs to -1 enabled full parallelization across CPU cores. Results show the parallel implementation achieved over 5× faster CPU times and 3× faster total run times compared to sequential tuning. However, test accuracy slightly dropped from 99.26% sequentially to 99.15% with parallelism, indicating a trade-off between evaluation efficiency and model performance. Still, the significant computational gains allow more extensive hyperparameter exploration within reasonable timeframes, outweighing the small accuracy decrease. Further analysis could better quantify this trade-off across different models, tuning techniques, tasks, and hardware.
文摘Parallel Knowledge Base Machine PKBM95 is a kind of special computer which is designed to improve the inference capability of production systems. Its hardware architecture is a multiprocessor, consisting of one microcomputer and four TRANSPUTERs. We will focus our discussion on the concentration-scattered inference model and the twice-conflict resolution strategy presented in the this paper, as well as the architecture and operating language of PKBM95. According to experiments, they are effective in improving the inference capability of the system.
