``ignore_const_cols`` --------------------- - Available in: GBM, DRF, Deep Learning, GLM, PCA, GLRM, Naïve-Bayes, K-Means - 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. Related Parameters ~~~~~~~~~~~~~~~~~~ - `ignored_columns `__ 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]] # 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)) .. code-block:: python 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)