``huber_alpha`` --------------- - Available in: GBM, Deep Learning - Hyperparameter: yes Description ~~~~~~~~~~~ The Huber loss function is a combination of the squared-error loss function and absolute-error loss function. It applies the squared-error loss for small deviations from the actual response value and the absolute-error loss for large deviations from the actual respone value. To activate this parameter you must set ``distribution=huber`` and specify the ``huber_alpha`` parameter, which dictates the threshold between quadratic and linear loss (i.e. the top percentile of error that should be considered as outliers). This value must be between 0 and 1 and defaults to 0.9. More information about the Huber loss function is available `here `__. Related Parameters ~~~~~~~~~~~~~~~~~~ - `distribution `__ Example ~~~~~~~ .. example-code:: .. code-block:: r library(h2o) h2o.init() # import the insurance dataset: # this dataset predicts the number of claims a policy holder will make # original dataset can be found at https://cran.r-project.org/web/packages/MASS/MASS.pdf insurance <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv") # set the predictor names and the response column name predictors <- colnames(insurance)[1:4] response <- 'Claims' # convert columns to factors insurance['Group'] <- as.factor(insurance['Group']) insurance['Age'] <- as.factor(insurance['Age']) # split into train and validation sets insurance.splits <- h2o.splitFrame(data = insurance, ratios = .8, seed = 1234) train <- insurance.splits[[1]] valid <- insurance.splits[[2]] # try using the `huber_alpha` parameter: # train your model, where you specify the distribution as huber # and the huber_alpha parameter insurance_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, validation_frame = valid, distribution = 'huber', huber_alpha = .9, seed = 1234) # print the MSE for validation set print(h2o.mse(insurance_gbm, valid = TRUE)) # grid over `huber_alpha` parameter # select the values for `huber_alpha` to grid over hyper_params <- list( huber_alpha = c(.2, .5, .8) ) # 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"} # build grid search with previously made GBM and hyperparameters grid <- h2o.grid(x = predictors, y = response, training_frame = train, validation_frame = valid, algorithm = "gbm", grid_id = "insurance_grid", distribution = "huber", hyper_params = hyper_params, seed = 1234) # Sort the grid models by MSE sortedGrid <- h2o.getGrid("insurance_grid", sort_by = "mse", decreasing = FALSE) sortedGrid .. code-block:: python import h2o from h2o.estimators.gbm import H2OGradientBoostingEstimator h2o.init() # import the insurance dataset: # this dataset predicts the number of claims a policy holder will make # original dataset can be found at https://cran.r-project.org/web/packages/MASS/MASS.pdf insurance = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv") # set the predictor names and the response column name predictors = insurance.columns[0:4] response = 'Claims' # convert columns to factors insurance['Group'] = insurance['Group'].asfactor() insurance['Age'] = insurance['Age'].asfactor() # split into train and validation sets train, valid = insurance.split_frame(ratios = [.8], seed = 1234) # try using the `huber_alpha` parameter: # initialize your estimator where you specify the distribution as huber # and the huber_alpha parameter insurance_gbm = H2OGradientBoostingEstimator(distribution="huber", huber_alpha = 0.9, seed =1234) # then train your model insurance_gbm.train(x = predictors, y = response, training_frame = train, validation_frame = valid) # print the MSE for the validation data print(insurance_gbm.mse(valid = True)) # Example of values to grid over for `huber_alpha` # import Grid Search from h2o.grid.grid_search import H2OGridSearch # select the values for `huber_alpha` to grid over hyper_params = {'huber_alpha': [.2, .5, .8]} # 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 insurance_gbm_2 = H2OGradientBoostingEstimator(distribution="huber", seed = 1234) # build grid search with previously made GBM and hyper parameters grid = H2OGridSearch(model = insurance_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 MSE sorted_grid = grid.get_grid(sort_by = 'mse', decreasing = False) print(sorted_grid)