![]() Grid search builds models for every combination of hyperparameter values that you specify. A Little Help?įor several years H2O has included grid search, also known as Cartesian Hyperparameter Search or exhaustive search. By looking at the models’ predictive performance, as measured by test-set, cross-validation or validation metrics, you select the best hyperparameter settings for your data and needs.Īs the number of hyperparameters and the lists of desired values increase this obviously becomes quite tedious and difficult to manage. This process of trying out hyperparameter sets by hand is called manual search. For example, for a tree-based model you might choose ntrees of (50, 100 and 200) and max_depth of (5, 10, 15 and 20) for a total of 3 x 4 = 12 models. The traditional method of selecting the values for your hyperparameters has been to individually train a number of models with different combinations of values, and then to compare the model performance to choose the best model. Selecting Hyperparameters Manually and With Cartesian Grid You can read much more on this topic in Chapter 7 of Elements of Statistical Learning from H2O advisors and Stanford professors Trevor Hastie and Rob Tibshirani with Jerome Friedman. The standard practice for evaluating a model found by cross-validation is to report for the that minimizes. Ideally you should use cross-validation or a validation set during training and then a final holdout test ( validation) dataset for model selection. Note that this is the same principle as, but subtly different from, overfitting during model training. Otherwise, the hyperparameter values that you choose will be too highly tuned to your selection data, and will not generalize as well as they could to new data. During the process of tuning the hyperparameters and selecting the best model you should avoid overfitting them to your training data. Overfitting not only applies to the model training process, but also to the model selection process. Overfitting is the phenomenon of fitting a model so thoroughly to your training data that it begins to memorize the fine details of that specific data, rather than finding general characteristics of that data which will also apply to future data on which you want to make predictions. However, you may want to choose a metric to compare your models based on your specific goals (e.g., maximizing AUC, minimizing log loss, minimizing false negatives, minimizing mean squared error, …). ![]() If you don’t know which to use, H2O will choose a good general-purpose metric for you based on the category of your model ( binomial or multinomial classification, regression, clustering, …). There are many different ways to measure model quality. You should choose values that reflect this for your search (e.g., powers of 10 or of 2) to ensure that you cover the most relevant parts of the hyperparameter space. Note that some hyperparameters, such as learning_rate, have a very wide dynamic range. You should look carefully at the values of the ones marked critical, while the secondary or expert ones are generally used for special cases or fine tuning. H2O provides some guidance by grouping the hyperparameters by their importance in the Flow UI. You should start with the most important hyperparameters for your algorithm of choice, for example ntrees and max_depth for the tree models or the hidden layers for Deep Learning. H2O contains good default values for many datasets, but to get the best performance for your data you will want to tune at least some of these hyperparameters to maximize the predictive performance of your models. This process is called hyperparameter optimization. We’d like to find a set of hyperparameter values which gives us the best model for our data in a reasonable amount of time. The set of all combinations of values for these knobs is called the hyperparameter space. These knobs are called hyperparameters to distinguish them from internal model parameters, such as GLM’s beta coefficients or Deep Learning’s weights, which get learned from the data during the model training process. Examples are the regularization settings alpha and lambda for Generalized Linear Modeling or ntrees and max_depth for Gradient Boosted Models. 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. 281:Ĭompared 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.Įven smarter means of searching the hyperparameter space are in the pipeline, but for most use cases random search does as well. H2O now has random hyperparameter search with time- and metric-based early stopping. ‘Til your good is better and your better is best.” – St.
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