``training_frame`` ------------------ - Available in: GBM, DRF, Deep Learning, GLM, GAM, PCA, GLRM, Naïve-Bayes, K-Means, Word2Vec, Stacked Ensembles, AutoML, XGBoost, Aggregator, CoxPH, Isolation Forest, Extended Isolation Forest, Uplift DRF, AdaBoost - Hyperparameter: no Description ~~~~~~~~~~~ The ``training_frame`` parameter specifies the dataset that you want to use when you are ready to build a model. Related Parameters ~~~~~~~~~~~~~~~~~~ - `model_id `__ - `validation_frame `__ Example ~~~~~~~ .. tabs:: .. code-tab:: r 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 `training_frame` parameter: # train your model, where you specify your 'x' predictors, your 'y' the response column # training_frame and validation_frame cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, validation_frame = valid, seed = 1234) # print the auc for your model print(h2o.auc(cars_gbm, valid = TRUE)) .. code-tab:: 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 `training_frame` parameter: # first initialize your estimator cars_gbm = H2OGradientBoostingEstimator(seed = 1234) # then train your model, where you specify your 'x' predictors, your 'y' the response column # training_frame and validation_frame cars_gbm.train(x = predictors, y = response, training_frame = train, validation_frame = valid) # print the auc for the validation data cars_gbm.auc(valid=True)