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Are there any advanced packages that allows automated tuning of hyperparameters for neural network and traditional machine learning algorithms like XGBoost, random forest (using method like Bayesian, random search etc. that could allow faster discovering of best parameters)? I heard of hyperopt but seems there are some problems, and not sure it can train traditional machine learning algorithms?

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    $\begingroup$ New packages come out all the time; here are two: FAR-HO, BayesianOptimization. I would recommend using random search when applicable; it is simple and performant. $\endgroup$ – Emre Jan 26 '18 at 6:49
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There are a number of methods to automate the optimisation of your hyper-parameters, such as GridSearch and RandomSearch which the article you linked discusses briefly.

The main reason to choose one over the other is if you want the best possible parameters, and don't care how long it takes to get them: go for GridSearch. On the other hand, if you don't want the optimisation to take a long time, but still want some good parameters then go for RandomSearch.

These two implementations in ScikitLearn aren't exactly "advanced packages" but they'll get the job done for any model in Scikit (Random Forest, MLPClassifiers, etc). Emre's comment also has some pretty cool advanced packages which are Scikit compatible or Tensorflow compatible.

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