``model_id`` ------------ - Available in: GBM, DRF, Deep Learning, GLM, PCA, GLRM, Naïve-Bayes, K-Means - Hyperparameter: no Description ~~~~~~~~~~~ When building a model, H2O automatically generates a destination key as a unique identifier for the model. You can optionally use this option to specify a custom name for your model. Related Parameters ~~~~~~~~~~~~~~~~~~ - `training_frame `__ - `validation_frame `__ Example ~~~~~~~ .. example-code:: .. code-block:: r library(h2o) h2o.init() # import the cars dataset: # this dataset is used to classify whether or not a car is economical based on # the car's displacement, power, weight, and acceleration, and the year it was made cars <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") # convert response column to a factor cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"]) # set the predictor names and the response column name predictors <- c("displacement","power","weight","acceleration","year") response <- "economy_20mpg" # split into train and validation sets cars.split <- h2o.splitFrame(data = cars,ratios = 0.8, seed = 1234) train <- cars.split[[1]] valid <- cars.split[[2]] # try using the `model_id` parameter: # train your model cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, validation_frame = valid, model_id = "first_model", seed = 1234) # print the model id cars_gbm@model_id # the model_id can also be used with checkpointing to continue training .. code-block:: python import h2o from h2o.estimators.gbm import H2OGradientBoostingEstimator h2o.init() # import the cars dataset: # this dataset is used to classify whether or not a car is economical based on # the car's displacement, power, weight, and acceleration, and the year it was made cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv") # convert response column to a factor cars["economy_20mpg"] = cars["economy_20mpg"].asfactor() # set the predictor names and the response column name predictors = ["displacement","power","weight","acceleration","year"] response = "economy_20mpg" # split into train and validation sets train, valid = cars.split_frame(ratios = [.8], seed = 1234) # try using the `model_id` parameter: # first initialize your estimator cars_gbm = H2OGradientBoostingEstimator(model_id = "first_model", seed = 1234) # training_frame and validation_frame cars_gbm.train(x = predictors, y = response, training_frame = train, validation_frame = valid) # print the model id cars_gbm.model_id # the model_id can also be used with checkpointing to continue training