``col_sample_rate`` ------------------- - Available in: GBM, DRF - Hyperparameter: yes Description ~~~~~~~~~~~ Specify the column sampling rate (y-axis). This acceptable value range is 0.0 to 1.0. Higher values may improve training accuracy. Test accuracy improves when either columns or rows are sampled. (For details, refer to “`Stochastic Gradient Boosting” (Friedman, 1999) `__). The following illustrates how column sampling is implemented for GBM and DRF. For an example model using: - 100-column dataset - ``col_sample_rate_per_tree=0.754`` - ``col_sample_rate=0.8`` (Samples 80% of columns per split) For each tree, the floor is used to determine the number of columns - in this example, (0.754 * 100)=75 out of 100 - that are randomly picked, and then the floor is used to determine the number of columns - in this case, (0.754 * 0.8 * 100)=60 - that are then randomly chosen for each split decision (out of the 75). Row and column sampling (``sample_rate`` and ``col_sample_rate``) can improve generalization and lead to lower validation and test set errors. Good general values for large datasets are around 0.7 to 0.8 (sampling 70-80 percent of the data) for both parameters. Column sampling per tree (``col_sample_rate_per_tree``) can also be used. Note that ``col_sample_rate_per_tree`` is multiplicative with ``col_sample_rate``, so setting both parameters to 0.8, for example, results in 64% of columns being considered at any given node to split. Related Parameters ~~~~~~~~~~~~~~~~~~ - `col_sample_rate_per_tree `__ - `col_sample_rate_change_per_level `__ - `sample_rate `__ 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 <- c("Origin", "Dest", "Year", "UniqueCarrier", "DayOfWeek", "Month", "Distance", "FlightNum") 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 `col_sample_rate` parameter: airlines.gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, validation_frame = valid, col_sample_rate =.7 , seed = 1234) # print the AUC for the validation data print(h2o.auc(airlines.gbm, valid = TRUE)) # Example of values to grid over for `col_sample_rate` hyper_params <- list( col_sample_rate = c(.3, .7, .8, 1) ) # this example uses cartesian grid search because the search space is small # and we want to see the performance of all models. For a larger search space use # random grid search instead: list(strategy = "RandomDiscrete") # this GBM uses early stopping once the validation AUC doesn't improve by at least 0.01% for # 5 consecutive scoring events grid <- h2o.grid(x = predictors, y = response, training_frame = train, validation_frame = valid, algorithm = "gbm", grid_id = "air_grid", hyper_params = hyper_params, stopping_rounds = 5, stopping_tolerance = 1e-4, stopping_metric = "AUC", search_criteria = list(strategy = "Cartesian"), seed = 1234) ## Sort the grid models by AUC sortedGrid <- h2o.getGrid("air_grid", sort_by = "auc", decreasing = TRUE) sortedGrid .. 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 = ["Origin", "Dest", "Year", "UniqueCarrier", "DayOfWeek", "Month", "Distance", "FlightNum"] response = "IsDepDelayed" # split into train and validation sets train, valid= airlines.split_frame(ratios = [.8], seed = 1234) # try using the `col_sample_rate` parameter: # initialize your estimator airlines_gbm = H2OGradientBoostingEstimator(col_sample_rate = .7, seed =1234) # then train your model airlines_gbm.train(x = predictors, y = response, training_frame = train, validation_frame = valid) # print the auc for the validation data print(airlines_gbm.auc(valid=True)) # Example of values to grid over for `col_sample_rate` # import Grid Search from h2o.grid.grid_search import H2OGridSearch # select the values for col_sample_rate to grid over hyper_params = {'col_sample_rate': [.3, .7, .8, 1]} # this example uses cartesian grid search because the search space is small # and we want to see the performance of all models. For a larger search space use # random grid search instead: {'strategy': "RandomDiscrete"} # initialize the GBM estimator # use early stopping once the validation AUC doesn't improve by at least 0.01% for # 5 consecutive scoring events airlines_gbm_2 = H2OGradientBoostingEstimator(seed = 1234, stopping_rounds = 5, stopping_metric = "AUC", stopping_tolerance = 1e-4) # build grid search with previously made GBM and hyper parameters grid = H2OGridSearch(model = airlines_gbm_2, hyper_params = hyper_params, search_criteria = {'strategy': "Cartesian"}) # train using the grid grid.train(x = predictors, y = response, training_frame = train, validation_frame = valid) # sort the grid models by decreasing AUC sorted_grid = grid.get_grid(sort_by = 'auc', decreasing = True) print(sorted_grid)