Hyperparameter Optimization in H2O: Grid Search, Random Search and the Future R-bloggers

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'Til your good is better and your better is best." H2O now has random hyperparameter search with time- and metric-based early stopping. Bergstra and Bengio[1] write on p. 281: Compared with neural networks configured by a pure grid search, we find that random search over the same domain is able to find models that are as good or better within a small fraction of the computation time. Even smarter means of searching the hyperparameter space are in the pipeline, but for most use cases random search does as well. Nearly all model algorithms used in machine learning have a set of tuning "knobs" which affect how the learning algorithm fits the model to the data.


skopt API documentation

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This example assumes basic familiarity with scikit-learn. Search for parameters of machine learning models that result in best cross-validation performance is necessary in almost all practical cases to get a model with best generalization estimate. A standard approach in scikit-learn is using GridSearchCV class, which takes a set of values for every parameter to try, and simply enumerates all combinations of parameter values. The complexity of such search grows exponentially with addition of new parameters. A more scalable approach is using RandomizedSearchCV, which however does not take advantage of the structure of a search space.


Automate Hyperparameter Tuning for your models

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Now, we create the search space for hyperparameters for our classifier. To do this, we end up using many of hyperopt built-in functions which define various distributions. As you can see in the code below, we use uniform distribution between 0.7 and 1 for our subsample hyperparameter. You need to provide different labels for each hyperparam you define. I generally add a x_ before my parameter name to create this label.


Hyp-RL : Hyperparameter Optimization by Reinforcement Learning

arXiv.org Machine Learning

Hyperparameter tuning is an omnipresent problem in machine learning as it is an integral aspect of obtaining the state-of-the-art performance for any model. Most often, hyperparameters are optimized just by training a model on a grid of possible hyperparameter values and taking the one that performs best on a validation sample (grid search). More recently, methods have been introduced that build a so-called surrogate model that predicts the validation loss for a specific hyperparameter setting, model and dataset and then sequentially select the next hyperparameter to test, based on a heuristic function of the expected value and the uncertainty of the surrogate model called acquisition function (sequential model-based Bayesian optimization, SMBO). In this paper we model the hyperparameter optimization problem as a sequential decision problem, which hyperparameter to test next, and address it with reinforcement learning. This way our model does not have to rely on a heuristic acquisition function like SMBO, but can learn which hyperparameters to test next based on the subsequent reduction in validation loss they will eventually lead to, either because they yield good models themselves or because they allow the hyperparameter selection policy to build a better surrogate model that is able to choose better hyperparameters later on. Experiments on a large battery of 50 data sets demonstrate that our method outperforms the state-of-the-art approaches for hyperparameter learning.


6 Techniques to Boost your Machine Learning Models AISOMA AG Frankfurt

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In the field of machine learning, hyperparameter optimization refers to the search for optimal hyperparameters. A hyperparameter is a parameter that is used to control the training algorithm and whose value, unlike other parameters, must be set before the model is actually trained. You can boost your machine learning Models with hyperparameter tuning/optimization. Hyperparameters contain the data that govern the training process itself. Your model parameters are optimized (you could say "tuned") by the training process: you run data through the operations of the model, compare the resulting prediction with the actual value for each data instance, evaluate the accuracy, and adjust until you find the best values.