model_id

• Available in: GBM, DRF, Deep Learning, GLM, GAM, HGLM, PCA, GLRM, Naïve-Bayes, K-Means, Word2Vec, Stacked Ensembles, XGBoost, Aggregator, CoxPH, Isolation Forest, Extended Isolation Forest, Uplift DRF, AdaBoost, Decision Tree, ANOVAGLM, ModelSelection

• 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

R

Python

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

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