``ignored_columns`` ------------------- - Available in: GBM, DRF, Deep Learning, GLM, PCA, GLRM, Naïve-Bayes, K-Means - Hyperparameter: no Description ~~~~~~~~~~~ There may be instances when your dataset includes information that you want to be ignored when building a model. Use the ``ignored_columns`` parameter to specify an array of column names that should be ignored. 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 ``ignored_columns=["type"]``, then the algorithm will only ignore the **Type** column and will not ignore the **Types** column. Related Parameters ~~~~~~~~~~~~~~~~~~ - `ignore_const_cols `__ Example ~~~~~~~ .. example-code:: .. code-block:: r library(h2o) h2o.init() # import the airlines dataset: # This dataset is used to classify whether a flight will be delayed 'YES' or not "NO" # original data can be found at http://www.transtats.bts.gov/ airlines <- h2o.importFile("http://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") # convert columns to factors airlines["Year"] <- as.factor(airlines["Year"]) airlines["Month"] <- as.factor(airlines["Month"]) airlines["DayOfWeek"] <- as.factor(airlines["DayOfWeek"]) airlines["Cancelled"] <- as.factor(airlines["Cancelled"]) airlines['FlightNum'] <- as.factor(airlines['FlightNum']) # set the predictor names and the response column name predictors <- colnames(airlines[1:9]) response <- "IsDepDelayed" # split into train and validation airlines.splits <- h2o.splitFrame(data = airlines, ratios = .8, seed = 1234) train <- airlines.splits[[1]] valid <- airlines.splits[[2]] # try using the `ignored_columns` parameter: col_list <- c('DepTime','CRSDepTime','ArrTime','CRSArrTime') # train your model airlines.gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, validation_frame = valid, seed = 1234) # print the auc for the validation data print(h2o.auc(airlines.gbm, valid = TRUE)) .. code-block:: python import h2o from h2o.estimators.gbm import H2OGradientBoostingEstimator h2o.init() # import the airlines dataset: # This dataset is used to classify whether a flight will be delayed 'YES' or not "NO" # original data can be found at http://www.transtats.bts.gov/ airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") # convert columns to factors airlines["Year"]= airlines["Year"].asfactor() airlines["Month"]= airlines["Month"].asfactor() airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() airlines["Cancelled"] = airlines["Cancelled"].asfactor() airlines['FlightNum'] = airlines['FlightNum'].asfactor() # set the predictor names and the response column name predictors = airlines.columns[:9] response = "IsDepDelayed" # split into train and validation sets train, valid= airlines.split_frame(ratios = [.8], seed = 1234) # try using the `ignored_columns` parameter: # create a list of column names to ignore col_list = ['DepTime','CRSDepTime','ArrTime','CRSArrTime'] # initialize the estimator and train the model airlines_gbm = H2OGradientBoostingEstimator(ignored_columns = col_list, seed =1234) airlines_gbm.train(x = predictors, y = response, training_frame = train, validation_frame = valid) # print the auc for the validation data airlines_gbm.auc(valid=True)