``y`` ----- - Available in: GBM, DRF, Deep Learning, GLM, Naïve-Bayes - Hyperparameter: no Description ~~~~~~~~~~~ Use this option to specify a response column (y-axis). The response column is the column that you are attempting to predict. For example, based on a set of parameters in a training dataset, will a new customber be more or less likely to purchase a product? Or based on some known variables, what is the likelihood that a flight will be delayed? In both cases, a model can be applied to a training frame and to a validation frame to predict the likely response. **Response Columns with DL and GBM Distribution** Response columns can be numeric or categorical, and they can be binomial or multiomial. If you are specifying a distribution type in DL or GBM, however, then keep in mind the following when defining a response column: - If the distribution is ``bernoulli``, the the response column must be 2-class categorical - If the distribution is ``multinomial``, the response column must be categorical. - If the distribution is ``poisson``, the response column must be numeric. - If the distribution is ``laplace``, the response column must be numeric. - If the distribution is ``tweedie``, the response column must be numeric. - If the distribution is ``gaussian``, the response column must be numeric. - If the distribution is ``huber``, the response column must be numeric. - If the distribution is ``gamma``, the response column must be numeric. - If the distribution is ``quantile``, the response column must be numeric. **Response Columns with GLM Family** In GLM, you can specify one of the following family options based on the response column type: - ``gaussian``: The data must be numeric (Real or Int). This is the default family. - ``binomial``: The data must be categorical 2 levels/classes or binary (Enum or Int). - ``quasibinomial``: The data must be numeric. - ``multinomial``: The data can be categorical with more than two levels/classes (Enum). - ``poisson``: The data must be numeric and non-negative (Int). - ``gamma``: The data must be numeric and continuous and positive (Real or Int). - ``tweedie``: The data must be numeric and continuous (Real) and non-negative. **Notes**: - The response column cannot be the same as the `fold_column `__. - For supervised learning, the response column cannot be the same as the `weights_column `__, and the response column must exist in both the training frame and in the validation frame. Related Parameters ~~~~~~~~~~~~~~~~~~ - `distribution `__ - `family `__ - `offset_column `__ - `weights_column `__ 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 `y` 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-block:: python import h2o from h2o.estimators.gbm import H2OGradientBoostingEstimator h2o.init() h2o.cluster().show_status() # 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 `y` 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)