Selecting the best configuration of hyperparameter values for a Machine Learning model yields directly in the performance of the model on the dataset. It is a laborious task that usually requires deep knowledge of the hyperparameter optimizations methods and the Machine Learning algorithms. Although there exist several automatic optimization techniques, these usually take significant resources ...
CoRRabs/1801.006882018Informal Publicationsjournals/corr/abs-1801-00688http://arxiv.org/abs/1801.00688https://dblp.org/rec/journals/corr/abs-1801-00688 URL#999074 ...
至于Hyperband，其主要思想是根据搜索时间优化随机搜索。 对于每个调谐器，可以为实验可重复性定义种子参数：SEED = 1。 随机搜寻 执行超参数调整的最直观方法是随机采样超参数组合并进行测试。这正是RandomSearch调谐器的功能！ 目标是优化功能。
A phenomenal answer. My only addition is that modern hyperparameter tuning has introduced better methods beyond grid and random search. Bayesian Optimization and Hyperband are two such techniques. Generally, successive halving techniques have been found to perform well. – Dave Liu Dec 9 '19 at 19:48
(i.e. a Python library). We then tune the hyperparameters of the XGBoost i.e. the Extreme Gradient Boosting algorithm on ten datasets by applying Random search, Randomized-Hyperopt, Hyperopt and Grid Search. The performances of each of these four techniques were compared by taking into account both the prediction accuracy and the execution time.
Optimizing an MLP with Hyperband¶ An example for the usage of a model-free Hyperband intensifier in SMAC. The configurations are randomly sampled. In this example, we use a real-valued budget in hyperband (number of epochs to train the MLP) and optimize the average accuracy on a 5-fold cross validation.
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