R语言H2O网格搜索：如何用最优模型在新数据上训练？

Great question—manually copying hyperparameters from a grid search best model is such a tedious, error-prone chore, so I’m glad you asked! The short answer is: yes, you absolutely can use the best model object directly for training on new data without retyping all those parameters. Here are two straightforward ways to do it:

1. Continue Training on the Existing Best Model #

If you want to keep building on the already trained best model (e.g., fine-tune it with more data or additional epochs), use H2O’s built-in continue_training() method. This preserves all the hyperparameters from your optimal model automatically:

# First, extract your best model from the grid search
# Replace "AUC" with your preferred evaluation metric (e.g., "logloss", "MSE")
best_model = your_grid_object.get_best_model(criterion="AUC")

# Continue training on your new dataset
# You can adjust training parameters like epochs if needed
updated_best_model = best_model.continue_training(
    training_frame=new_training_data,
    epochs=best_model.epochs + 50  # Add 50 more epochs to the original training
)

2. Train a Fresh Model with the Best Hyperparameters #

If you want to start a new training run from scratch using the optimal parameters (rather than continuing the existing model), you can extract the model’s parameters programmatically instead of typing them manually:

# Extract the best model's parameters
best_params = best_model.params

# Clean up parameters to remove auto-generated fields (like model_id) that aren't needed for training
training_ready_params = {
    param_name: param_details["actual"]
    for param_name, param_details in best_params.items()
    if param_name not in ["model_id", "training_frame", "validation_frame"]
}

# Train a new model on your new dataset using the pre-extracted optimal parameters
fresh_optimal_model = h2o.gbm(
    training_frame=new_training_data,
    validation_frame=new_validation_data,  # Optional, if you have it
    **training_ready_params
)

Both methods eliminate the need for manual parameter copying, saving you time and reducing the risk of typos or missed parameters.

内容的提问来源于stack exchange，提问作者Edward