``objective_epsilon`` --------------------- - Available in: GLM, GAM - Hyperparameter: no Description ~~~~~~~~~~~ GLM and GAM include three criteria outside of ``max_iterations`` that define and check for convergence during logistic regression: - ``beta_epsilon``: Converge if the beta change is less than this value (or if beta stops changing). This is used by solvers. - ``gradient_epsilon``: Converge if the gradient value change is less than this value (using L-infinity norm). This is used when ``solver=L-BFGS``. - ``objective_epsilon``: Converge if the relative objective value changes (for example, (old_val - new_val)/old_val). This is used by all solvers. The default for these options is based on a heurisitic: - ``beta_epsilon``: The default for ``beta_epsilon`` is 1e-4. - ``gradient_epsilon``: If ``lambda_search`` is set to False and lambda is equal to zero, the default value of ``gradient_epsilon`` is equal to .000001; otherwise the default value is .0001. If ``lambda_search`` is set to True, then the conditional values above are 1E-8 and 1E-6 respectively. - ``objective_epsilon``: If ``lambda_search=True``, then the default value of ``objective_epsilon`` is .0001. If ``lambda_search=False`` and lambda is equal to zero, then the default value of ``objective_epsilon`` is .000001. For any other value of lambda, the default value of ``objective_epsilon`` is set to .0001. Related Parameters ~~~~~~~~~~~~~~~~~~ - `beta_epsilon `__ - `gradient_epsilon `__ - `max_iterations `__ - `solver `__ Example ~~~~~~~ .. tabs:: .. code-tab:: r R library(h2o) h2o.init() # import the boston dataset: # this dataset looks at features of the boston suburbs and predicts median housing prices # the original dataset can be found at https://archive.ics.uci.edu/ml/datasets/Housing boston <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") # set the predictor names and the response column name predictors <- colnames(boston)[1:13] # set the response column to "medv", the median value of owner-occupied homes in $1000's response <- "medv" # convert the chas column to a factor (chas = Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)) boston["chas"] <- as.factor(boston["chas"]) # split into train and validation sets boston_splits <- h2o.splitFrame(data = boston, ratios = 0.8) train <- boston_splits[[1]] valid <- boston_splits[[2]] # try using the `objective_epsilon` parameter: # train your model, where you specify objective_epsilon boston_glm <- h2o.glm(x = predictors, y = response, training_frame = train, validation_frame = valid, objective_epsilon = 1e-3) # print the mse for the validation data print(h2o.mse(boston_glm, valid = TRUE)) .. code-tab:: python import h2o from h2o.estimators.glm import H2OGeneralizedLinearEstimator h2o.init() # import the boston dataset: # this dataset looks at features of the boston suburbs and predicts median housing prices # the original dataset can be found at https://archive.ics.uci.edu/ml/datasets/Housing boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") # set the predictor names and the response column name predictors = boston.columns[:-1] # set the response column to "medv", the median value of owner-occupied homes in $1000's response = "medv" # convert the chas column to a factor (chas = Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)) boston['chas'] = boston['chas'].asfactor() # split into train and validation sets train, valid = boston.split_frame(ratios = [.8]) # try using the `objective_epsilon` parameter: # initialize the estimator then train the model boston_glm = H2OGeneralizedLinearEstimator(objective_epsilon = 1e-3) boston_glm.train(x = predictors, y = response, training_frame = train, validation_frame = valid) # print the mse for validation set print(boston_glm.mse(valid=True))