ignore_const_cols

  • Available in: GBM, DRF, Deep Learning, GLM, GAM, HGLM, PCA, GLRM, Naïve-Bayes, K-Means, XGBoost, Aggregator, Isolation Forest, Extended Isolation Forest, Uplift DRF, AdaBoost, Decision Tree, ANOVAGLM, ModelSelection

  • 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

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]]

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

# try using the `ignore_const_cols` parameter (boolean parameter):
# train your model
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
from h2o.estimators.gbm import H2OGradientBoostingEstimator
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):
# first initialize your estimator
cars_gbm = H2OGradientBoostingEstimator(seed = 1234, ignore_const_cols = True)

# then train your model
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)