Archives for Bayesian optimisation

13 Mar

Guide to Scalable and Robust Bayesian Optimization with Dragonfly

Dragonfly, an open-source python framework for scalable and robust Bayesian optimization, is developed by researchers from Carnegie Mellon University, Pittsburgh : Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabas Poczos, Eric P. Xing. The paper was submitted to Journal of Machine Learning Research in April 2020 titled “…

The post Guide to Scalable and Robust Bayesian Optimization with Dragonfly appeared first on Analytics India Magazine.

13 Mar

Guide to Scalable and Robust Bayesian Optimization with Dragonfly

Dragonfly, an open-source python framework for scalable and robust Bayesian optimization, is developed by researchers from Carnegie Mellon University, Pittsburgh : Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabas Poczos, Eric P. Xing. The paper was submitted to Journal of Machine Learning Research in April 2020 titled “…

The post Guide to Scalable and Robust Bayesian Optimization with Dragonfly appeared first on Analytics India Magazine.

08 Aug

Implementing Bayesian Optimization On XGBoost: A Beginner’s Guide 

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Probability is an integral part of Machine Learning algorithms. We use it to predict the outcome of regression or classification problems. We apply what’s known as conditional probability or Bayes Theorem along with Gaussian Distribution to predict the probability of a class or a value, given a condition. The pair is also used in optimising…

The post Implementing Bayesian Optimization On XGBoost: A Beginner’s Guide  appeared first on Analytics India Magazine.