h2o.get_best_model: Get best model of a given family/algorithm for a given...

In h2o: R Interface for the 'H2O' Scalable Machine Learning Platform

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 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 documentation built on May 29, 2024, 4:26 a.m.