x

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

  • Hyperparameter: no

Description

There may be instances when your dataset includes more information than you want to be included when building a model. Use the x parameter to specify a vector containing the names or indices of the predictor variables to use when building the model. If x is missing, then all columns except y are used.

Note that this is a strict parameter that takes into account the exact string of the column name. So, for example, if your dataset includes one column named Type and another column named Types, and you specify x=["type"], then the algorithm will only include the Type column and will ignore the Types column.

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)