``learn_rate`` --------------- - Available in: GBM - Hyperparameter: Description ~~~~~~~~~~~ This option is used to specify the rate at which GBM learns when building a model. Lower learning rates are generally better, but then more trees are required (using ``ntrees``) to achieve the same level of fit as if you had used a higher learning rate. This method helps avoid overfitting. You can use this option along with the ``learn_rate_annealing`` option to reduce the learning rate by a specified factor for every tree. This can help speed of convergence without sacrificing too much accuracy. For faster scans, use (for example) ``learn_rate=0.5`` and ``learn_rate_annealing=0.99``. Related Parameters ~~~~~~~~~~~~~~~~~~ - `learn_rate_annealing `__ - `ntrees `__ Example ~~~~~~~ .. example-code:: .. code-block:: r library(h2o) h2o.init() # import the titanic dataset: # This dataset is used to classify whether a passenger will survive '1' or not '0' # original dataset can be found at https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/Titanic.html titanic <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") # convert response column to a factor titanic['survived'] <- as.factor(titanic['survived']) # set the predictor names and the response column name # predictors include all columns except 'name' and the response column ("survived") predictors <- setdiff(colnames(titanic), colnames(titanic)[2:3]) response <- "survived" # split into train and validation titanic.splits <- h2o.splitFrame(data = titanic, ratios = .8, seed = 1234) train <- titanic.splits[[1]] valid <- titanic.splits[[2]] # try using the `learn_rate` parameter: # because we use a small learning_rate, we set ntrees to a much higer number # early stopping makes it okay to use 'more than enough' trees titanic.gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, validation_frame = valid, ntrees = 10000, learn_rate = .01, # use early stopping once the validation AUC doesn't improve by at least 0.01% # for 5 consecutive scoring events stopping_rounds = 5, stopping_tolerance = 1e-4, stopping_metric = "AUC", seed = 1234) # print the auc for the validation data print(h2o.auc(titanic.gbm, valid = TRUE)) .. code-block:: python import h2o from h2o.estimators.gbm import H2OGradientBoostingEstimator h2o.init() # import the titanic dataset: # This dataset is used to classify whether a passenger will survive '1' or not '0' # original dataset can be found at https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/Titanic.html titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv") # convert response column to a factor titanic['survived'] = titanic['survived'].asfactor() # set the predictor names and the response column name # predictors include all columns except 'name' and the response column ("survived") predictors = titanic.columns del predictors[1:3] response = 'survived' # split into train and validation sets train, valid = titanic.split_frame(ratios = [.8], seed = 1234) # try using the `learn_rate` parameter: # because we use a small learning_rate, we set ntrees to a much higer number # early stopping makes it okay to use 'more than enough' trees # initialize your estimator, then train the model titanic_gbm = H2OGradientBoostingEstimator(ntrees = 10000, learn_rate = 0.01, # use early stopping once the validation AUC doesn't improve # by at least 0.01% for 5 consecutive scoring events stopping_rounds = 5, stopping_metric = "AUC", stopping_tolerance = 1e-4, seed = 1234) titanic_gbm.train(x = predictors, y = response, training_frame = train, validation_frame = valid) # print the auc for the validation data print(titanic_gbm.auc(valid=True))