# ignore_const_cols¶

• Available in: GBM, DRF, Deep Learning, GLM, GAM, PCA, GLRM, Naïve-Bayes, K-Means, XGBoost, Aggregator, Isolation Forest

• Hyperparameter: no

## Description¶

Unlike the ignored_columns parameter, which allows you to specify the column name or names to ignore when building a model, the ignore_const_cols option allows you to specify that the algorithm should ignore all constant columns (columns that include the same value). This allows you to speed up training by ignoring columns from which no information can be gained.

This option is enabled by default.

## Example¶

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]]

# add a few constant columns
cars["const_1"] = 6
cars["const_2"] = 7

# try using the ignore_const_cols parameter (boolean parameter):
cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train,
validation_frame = valid, ignore_const_cols = TRUE, seed = 1234)

# print the auc for your model
print(h2o.auc(cars_gbm, valid = TRUE))

import 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.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"

# add a few constant columns
cars["const_1"] = 6
cars["const_2"] = 7

# split into train and validation sets
train, valid = cars.split_frame(ratios = [.8], seed = 1234)

# try using the ignore_const_cols parameter (boolean parameter):