Model Tuning with H2O Grid Search

Optimizing the hyperparameters for a given algorithm and dataset is a crucial step in the machine learning pipeline known as model tuning. Model performance is significantly impacted by the values chosen for hyperparameters like the learning rate, number of trees, early stopping, regularization parameter, and the number of hidden layers for deep learning models.

We can improve the accuracy and generalization ability of the model and make better predictions and more effective decisions through model tuning. It’s essential to avoid overfitting, which occurs when the model is overly complex and fits the training data too closely, leading to poor performance on new data. The optimal hyperparameters enable a model that is adequately complex and able to generalize to new data. Model tuning is crucial to achieving successful machine learning outcomes.

Let’s understand how we can tune our machine learning models with the help of H2O grid search.

Introduction to H2O grid search

H2O grid search is a tool for hyperparameter tuning in H2O. It allows the user to perform a systematic search over a specified hyperparameter space in order to identify the optimal set of hyperparameters to maximize a performance metric.

H2O supports two types of grid search—traditional and random.

• Traditional grid search: We specify a set of values for each hyperparameter, and H2O will train a model for every possible combination of hyperparameter values. This can lead to a large number of models being trained, which can be time-consuming and computationally expensive.

• Random grid search: We specify a range of values for each hyperparameter, and H2O will randomly sample from these ranges to train a set of models. This can be more efficient than traditional grid search, especially when searching over a large hyperparameter space. We also need to specify a stopping criterion, which controls when the random grid search is completed. The stopping criterion can be specified in terms of maximum runtime or maximum number of models, and we can also define a stopping criterion based on performance metrics.

Setting up H2O grid search

The H2O grid search has four parameters, and in order to use it, we need at least three of them. Here’s a list of all four parameters:

• model: The model that we want to tune.

• hyper_params: A string of model parameters and a list of values to be explored by the grid search.

• grid_id: This is optional, and if we choose not to specify it, an ID will automatically be generated.

• search_criteria: This specifies a cartesian or random search.

Here’s an example code snippet that shows the setup of an H2O grid search object for the H2OGradientBoostingEstimator model: