1 2 3 4 5 6 7 | ezr.h2o_gbm_grid(train_df, valid_df = NULL, xvars = names(train_df),
yvar = "target", grid_id = "gbm_grid", prescreengbm = TRUE,
novalid_ok = FALSE, prescreen_keepvars_criteria = "percent",
prescreen_keepvars_threshold = 0.005, xval = TRUE, folds = 5,
keep_cross_validation_predictions = TRUE, max_models = 1,
learnrate = 0.025, max_min_runtime = 15, ntrees = 125,
seed = 2018, ...)
|
train_df |
h2o dataframe |
valid_df |
a validation dataframe. Default is NULL. If NULL it the train_df will be split into 80/20 split and the 20 \itemxvarsdefault is everything in training df \itemyvartarget \itemgrid_idgrid id to use. Default is gbm_grid \itemprescreengbmDefault is TRUE. Should a pre-screen be run to eliminate excess variables? This will run a gbm with default params, and be used to eliminate variables before re-training. This is to prevent against 100s of variables with 0.001 or similar importance criteria in model. \itemprescreen_keepvars_criteriaValid values are 'percent' and 'number' Default is 'percent' importance. Number refers to how many variables such as 5/10/100 \itemprescreen_keepvars_thresholdDefault threshold is 0.01 for percent for retention. Enter an integer for 'count'. If the value is <= 1 and the <prescreen_keepvars_criteria> is equal to 'number' then this will default to 25. \itemxvalDefault is TRUE. \itemfoldsDefault is 5 \itemkeep_cross_validation_predictionsDefault is FALSE \itemmax_modelsDefault is 1. If value is 1, then a default GBM will run \itemlearnrateDefault is 0.05. You can enter a vector c(0.01, 0.05) \itemmax_min_runtimeHow many minutes can this run for? Default is 15min \itemntreesDefault is 100. \itemseedDefault is 2018 \item...Additional inputs... \itemnotvalid_okFALSE by default. If TRUE, then there is no validation dataset when only training dataset is entered. |
Returns a grid of models Off the shelf grid search for GBM w/ hyper parameters. library(h2o) h2o.init() h2odf = as.h2o(dataset_telco_churn_from_kaggle) example_grid_search=ezr.h2o_gbm_grid(train_df = h2odf, yvar='Churn', max_models = 11)
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