Note that if neither cross-validation nor a validation frame is used in the grid search, then the training metrics will display in the "get grid" output. If a validation frame is passed to the grid, and nfolds = 0, then the validation metrics will display. However, if nfolds > 1, then cross-validation metrics will display even if a validation frame is provided.
h2o.getGrid(grid_id, sort_by, decreasing)
| grid_id | ID of existing grid object to fetch |
|---|---|
| sort_by | Sort the models in the grid space by a metric. Choices are "logloss", "residual_deviance", "mse", "auc", "accuracy", "precision", "recall", "f1", etc. |
| decreasing | Specify whether sort order should be decreasing |
# NOT RUN { library(h2o) library(jsonlite) h2o.init() iris.hex <- as.h2o(iris) h2o.grid("gbm", grid_id = "gbm_grid_id", x = c(1:4), y = 5, training_frame = iris.hex, hyper_params = list(ntrees = c(1,2,3))) grid <- h2o.getGrid("gbm_grid_id") # Get grid summary summary(grid) # Fetch grid models model_ids <- grid@model_ids models <- lapply(model_ids, function(id) { h2o.getModel(id)}) # }