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- [ ] Look for information on automatic hyperparameter tuning optimization and its viability for our project
- [ ] Define hyperparameters to be optimized for
- [ ] Test new methods like population-b…
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Creating projects based on MNIST or other standard datasets to show the real-life importance of cross-validation and optimization techniques rather than just using and fitting the models directly onto…
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참조
* [nonparametric-methods.pdf](https://github.com/sweaterr/bayesian-methods-in-machine-learning/files/1730282/nonparametric-methods.pdf)
* [gaussian-processes.pdf](https://github.com/sweaterr/b…
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For many applications, one needs to pass a function that evaluates the cost (or the log-posterior) from the control vector. For instance:
1. MCMC sampling: samples=MCMC(logpost_function, starting_poi…
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Hello everyone,
I was wondering if anyone has worked on this subject. I will soon go about and try to test this out but before that I thought I'd ask for past experience.
I was thinking that su…
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hi all,
I've been trying to create a back testing framework for a time series analysis that involves tuning a random forest with Bayesian optimization monthly for the last 10 years. each month is i…
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Hi,
I recently tried to use the `DiscreteEuclideanDomain` in a Bayesian optimization (in ask-tell mode), but I am not sure how to combine it with the other objects in `dragonfly`. Here is how I in…
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Perhaps there is something to learn from this [paper](https://arxiv.org/abs/1812.06855).
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# Hyperparameters Tuning for XGBoost using Bayesian Optimization | Dr.Data.King
How to tune your XGBoost model hyperparameters? How to set up parallel computing for your model training which may take…