R package to tune parameters for machine learning(Support Vector Machine, Random Forest, and Xgboost), using bayesian optimization with gaussian process
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Updated
Dec 13, 2019 - R
R package to tune parameters for machine learning(Support Vector Machine, Random Forest, and Xgboost), using bayesian optimization with gaussian process
Streamlined Estimation for Static, Dynamic and Stochastic Treatment Regimes in Longitudinal Data
Machine Learning Hyper-parameter Tuning processes
Monte Carlo Penalty Selection for graphical lasso
we fit various splines to model the COVID-19 daily positive case numbers in Florida from 3/3/20 – 3/7/21.
Modified gap statistic (gap-com) for regularization selection of sparse networks. This method is aimed for complex network estimation.
Dataset preprocessed, tuned and trained using Support Vector Machine
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