h2o.get_best_model function

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

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

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.

Returns

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)
  • Maintainer: Tomas Fryda
  • License: Apache License (== 2.0)
  • Last published: 2024-01-11