h2o: h2o.get_best_model – R documentation

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h2o

h2o.get_best_model

Get best model of a given family/algorithm for a given criterion from an AutoML object.

Description

Get best model of a given family/algorithm for a given criterion from an AutoML object.

Usage

h2o.get_best_model(
  object,
  algorithm = c("any", "basemodel", "deeplearning", "drf", "gbm", "glm",
    "stackedensemble", "xgboost"),
  criterion = c("AUTO", "AUC", "AUCPR", "logloss", "MAE", "mean_per_class_error",
    "deviance", "MSE", "predict_time_per_row_ms", "RMSE", "RMSLE", "training_time_ms")
)

Arguments

object

H2OAutoML object

algorithm

One of "any", "basemodel", "deeplearning", "drf", "gbm", "glm", "stackedensemble", "xgboost"

criterion

Criterion can be one of the metrics reported in the leaderboard. If set to NULL, the same ordering as in the leaderboard will be used. Avaliable criteria:

Regression metrics: deviance, RMSE, MSE, MAE, RMSLE
Binomial metrics: AUC, logloss, AUCPR, mean_per_class_error, RMSE, MSE
Multinomial metrics: mean_per_class_error, logloss, RMSE, MSE, AUC, AUCPR

The following additional leaderboard information can be also used as a criterion:

'training_time_ms': column providing the training time of each model in milliseconds (doesn't include the training of cross validation models).
'predict_time_per_row_ms': column providing the average prediction time by the model for a single row.

Value

An H2OModel or NULL if no model of a given family is present

Examples

## Not run: 
library(h2o)
h2o.init()
prostate_path <- system.file("extdata", "prostate.csv", package = "h2o")
prostate <- h2o.importFile(path = prostate_path, header = TRUE)
y <- "CAPSULE"
prostate[,y] <- as.factor(prostate[,y])  #convert to factor for classification
aml <- h2o.automl(y = y, training_frame = prostate, max_runtime_secs = 30)
gbm <- h2o.get_best_model(aml, "gbm")

## End(Not run)

h2o

R Interface for the 'H2O' Scalable Machine Learning Platform

v3.32.1.2

Apache License (== 2.0)

Authors

Erin LeDell [aut, cre], Navdeep Gill [aut], Spencer Aiello [aut], Anqi Fu [aut], Arno Candel [aut], Cliff Click [aut], Tom Kraljevic [aut], Tomas Nykodym [aut], Patrick Aboyoun [aut], Michal Kurka [aut], Michal Malohlava [aut], Ludi Rehak [ctb], Eric Eckstrand [ctb], Brandon Hill [ctb], Sebastian Vidrio [ctb], Surekha Jadhawani [ctb], Amy Wang [ctb], Raymond Peck [ctb], Wendy Wong [ctb], Jan Gorecki [ctb], Matt Dowle [ctb], Yuan Tang [ctb], Lauren DiPerna [ctb], Tomas Fryda [ctb], H2O.ai [cph, fnd]

Initial release

2021-04-29