``nbins_top_level`` ------------------- - Available in: GBM, DRF - Hyperparameter: yes Description ~~~~~~~~~~~ For numerical columns (real/int), the ``nbins_top_level`` option is the number of bins to use at the top of each tree. It then divides by 2 at each ensuing level to find a new number. This option defaults to 1024 and is used with `nbins `_, which controls when the algorithm stops dividing by 2. To make a model more general, decrease ``nbins_top_level`` and ``nbins_cats``. To make a model more specific, increase ``nbins`` and/or ``nbins_top_level`` and ``nbins_cats``. Keep in mind that increasing ``nbins_cats`` can lead to in `overfitting `__ on the training set. Related Parameters ~~~~~~~~~~~~~~~~~~ - `nbins `__ - `nbins_cats `__ Example ~~~~~~~ .. example-code:: .. code-block:: r library(h2o) h2o.init() # import the EEG dataset: # All data is from one continuous EEG measurement with the Emotiv EEG Neuroheadset. # The duration of the measurement was 117 seconds. The eye state was detected via a camera during the # EEG measurement and added later manually to the file after analysing the video frames. # '1' indicates the eye-closed and '0' the eye-open state. All values are in chronological # order with the first measured value at the top of the data. # original dataset can be found at the UCI Machine Learning Repository http://archive.ics.uci.edu/ml/datasets/EEG+Eye+State eeg <- h2o.importFile("https://h2o-public-test-data.s3.amazonaws.com/smalldata/eeg/eeg_eyestate.csv") # convert response column to a factor eeg['eyeDetection'] <- as.factor(eeg['eyeDetection']) # set the predictor names and the response column name predictors <- colnames(eeg)[1:(length(eeg)-1)] response <- "eyeDetection" # split into train and validation eeg.splits <- h2o.splitFrame(data = eeg, ratios = .8, seed = 1234) train <- eeg.splits[[1]] valid <- eeg.splits[[2]] # try a range of nbins_top_level: bin_num = c(32, 64, 128, 256, 512, 1024, 2048, 4096) label = c("32", "64", "128", "256", "512", "1024", "2048", "4096") lapply(seq_along(1:length(bin_num)),function(num) { eeg.gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, validation_frame = valid, nbins_top_level = bin_num[num], nfolds = 5, seed = 1234) # print the value used and AUC score for train and valid print(paste(label[num], 'training score', h2o.auc(eeg.gbm, train = TRUE))) print(paste(label[num], 'validation score', h2o.auc(eeg.gbm, valid = TRUE))) }) # Example of values to grid over for `nbins_top_level` hyper_params <- list( nbins_top_level = c(32, 64, 128, 256, 512, 1024, 2048, 4096) ) # 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 = "eeg_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("eeg_grid", sort_by = "auc", decreasing = TRUE) sortedGrid .. code-block:: python import h2o from h2o.estimators.gbm import H2OGradientBoostingEstimator h2o.init() h2o.cluster().show_status() # import the EEG dataset: # All data is from one continuous EEG measurement with the Emotiv EEG Neuroheadset. # The duration of the measurement was 117 seconds. The eye state was detected via a camera during the # EEG measurement and added later manually to the file after analysing the video frames. # '1' indicates the eye-closed and '0' the eye-open state. All values are in chronological # order with the first measured value at the top of the data. # original dataset can be found at the UCI Machine Learning Repository http://archive.ics.uci.edu/ml/datasets/EEG+Eye+State eeg = h2o.import_file("https://h2o-public-test-data.s3.amazonaws.com/smalldata/eeg/eeg_eyestate.csv") # convert response column to a factor eeg['eyeDetection'] = eeg['eyeDetection'].asfactor() # set the predictor names and the response column name predictors = eeg.columns[:-1] response = 'eyeDetection' # split into train and validation sets train, valid = eeg.split_frame(ratios = [.8], seed = 1234) # try a range of values for `nbins_top_level` # we start at 32 because the default for nbins is 20, and nbins_top_level # must be greater than nbins bin_num = [32, 64, 128, 256, 512, 1024, 2048, 4096] label = ["32", "64", "128", "256", "512", "1024", "2048", "4096"] for key, num in enumerate(bin_num): # initialize the GBM estimator and set a seed for reproducibility eeg_gbm = H2OGradientBoostingEstimator(nbins_top_level = num, seed = 1234) eeg_gbm.train(x = predictors, y = response, training_frame = train, validation_frame = valid) # print the value used and AUC score for train and validation sets print(label[key], 'training score', eeg_gbm.auc(train = True)) print(label[key], 'validation score', eeg_gbm.auc(valid = True)) # Example of values to grid over for `nbins_top_level` # import Grid Search from h2o.grid.grid_search import H2OGridSearch # select the values for `nbins_top_level` to grid over hyper_params = {'nbins_top_level': [32, 64, 128, 256, 512, 1024, 2048, 4096]} # 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 eeg_gbm_2 = H2OGradientBoostingEstimator(stopping_rounds = 5, stopping_metric = "AUC", stopping_tolerance = 1e-4, seed = 1234) # build grid search with previously made GBM and hyper parameters grid = H2OGridSearch(model = eeg_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)