y

  • Available in: GBM, DRF, Deep Learning, GLM, GAM, HGLM, Naïve-Bayes, Stacked Ensembles, AutoML, XGBoost, Aggregator, Uplift DRF, AdaBoost, Decision Tree, ANOVAGLM, ModelSelection

  • 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.

  • ordinal: Requires a categorical response with at least 3 levels. (For 2-class problems use family=”binomial”.)

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.

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 `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))

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)