``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)