``min_rows`` ------------ - Available in: GBM, DRF - Hyperparameter: yes Description ~~~~~~~~~~~ This option specifies the minimum number of observations for a leaf in order to split. For example, if a user specifies ``min_rows = 500``, and the data has 500 TRUEs and 400 FALSEs, then the algorithm won’t split because it requires 500 responses on both sides. In addition, a single tree will stop splitting when there are no more splits that satisfy the ``min_rows`` parameter, if it reaches ``max_depth``, or if there are no splits that satisfy this ``min_split_improvement`` parameter. The default value for ``min_rows`` is 10, so this option rarely affects the GBM splits because GBMs are typically shallow, but the concept still applies. Related Parameters ~~~~~~~~~~~~~~~~~~ - `max_depth `__ - `min_split_improvement `__ 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]] # try using the `min_rows` parameter: cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, validation_frame = valid, min_rows = 16, seed = 1234) # print the auc for your model print(h2o.auc(cars_gbm, valid = TRUE)) # Example of values to grid over for `min_rows`: hyper_params <- list( min_rows = seq(1,20,1) ) # 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 = "cars_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("cars_grid", sort_by = "auc", decreasing = TRUE) sortedGrid .. 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" # split into train and validation sets train, valid = cars.split_frame(ratios = [.8], seed = 1234) # try turning on the `min_rows` parameter: # initialize your estimator cars_gbm = H2OGradientBoostingEstimator(min_rows = 16, seed = 1234) # then train your model cars_gbm.train(x = predictors, y = response, training_frame = train, validation_frame = valid) # print the auc for the validation data print(cars_gbm.auc(valid=True)) # Example of values to grid over for `min_rows` # import Grid Search from h2o.grid.grid_search import H2OGridSearch # select the values for `min_rows` to grid over hyper_params = {'min_rows': list(range(1,21))} # 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 cars_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 = cars_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, seed = 1234) # sort the grid models by decreasing AUC sorted_grid = grid.get_grid(sort_by = 'auc', decreasing = True) print(sorted_grid)