Get a grid object from H2O distributed K/V store.
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, verbose = FALSE)
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 |
verbose |
Controls verbosity of the output, if enabled prints out error messages for failed models (default: FALSE) |
## Not run:
library(h2o)
library(jsonlite)
h2o.init()
iris_hf <- as.h2o(iris)
h2o.grid("gbm", grid_id = "gbm_grid_id", x = c(1:4), y = 5,
training_frame = iris_hf, 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)})
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.