.. _stopping_tolerance: ``stopping_tolerance`` ---------------------- - Available in: GBM, DRF, Deep Learning, GLM, GAM, AutoML, XGBoost, Isolation Forest - Hyperparameter: yes Description ~~~~~~~~~~~ This option specifies the tolerance value by which a model must improve before training ceases. For example, given the following options: - ``stopping_rounds=3`` - ``stopping_metric=misclassification`` - ``stopping_tolerance=1e-3`` then the moving average for last 4 stopping rounds is calculated (the first moving average is reference value for other 3 moving averages to compare). The model will stop if the **ratio** between the best moving average and reference moving average is more or equal **1-1e-3** (the misclassification is the less the better metric, for the more the better metrics the ratio have to be less or equal **1+1e-3** to stop). These stopping options are used to increase performance by restricting the number of models that get built. **Notes**: - ``stopping_rounds`` must be enabled for ``stopping_metric`` or ``stopping_tolerance`` to work. - For all supported algorithms except AutoML and Isolation Forest, this value defaults to 0.001. In AutoML, this value defaults to 0.001 if the dataset is at least 1 million rows; otherwise it defaults to a bigger value determined by the size of the dataset and the non-NA-rate. In Isolation Forest, this value defaults to 0.01. Related Parameters ~~~~~~~~~~~~~~~~~~ - `stopping_metric `__ - `stopping_rounds `__ Example ~~~~~~~ .. tabs:: .. code-tab:: r R library(h2o) h2o.init() # import the airlines dataset: # This dataset is used to classify whether a flight will be delayed 'YES' or not "NO" # original data can be found at http://www.transtats.bts.gov/ airlines <- h2o.importFile("http://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") # convert columns to factors airlines["Year"] <- as.factor(airlines["Year"]) airlines["Month"] <- as.factor(airlines["Month"]) airlines["DayOfWeek"] <- as.factor(airlines["DayOfWeek"]) airlines["Cancelled"] <- as.factor(airlines["Cancelled"]) airlines['FlightNum'] <- as.factor(airlines['FlightNum']) # set the predictor names and the response column name predictors <- c("Origin", "Dest", "Year", "UniqueCarrier", "DayOfWeek", "Month", "Distance", "FlightNum") response <- "IsDepDelayed" # split into train and validation airlines_splits <- h2o.splitFrame(data = airlines, ratios = 0.8, seed = 1234) train <- airlines_splits[[1]] valid <- airlines_splits[[2]] # try using the `stopping_tolerance` metric: # train your model, where you specify the stopping_metric, stopping_rounds, # and stopping_tolerance airlines_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, validation_frame = valid, stopping_metric = "AUC", stopping_rounds = 3, stopping_tolerance = 1e-2, seed = 1234) # print the auc for the validation data print(h2o.auc(airlines_gbm, valid = TRUE)) .. code-tab:: python import h2o from h2o.estimators.gbm import H2OGradientBoostingEstimator h2o.init() h2o.cluster().show_status() # import the airlines dataset: # This dataset is used to classify whether a flight will be delayed 'YES' or not "NO" # original data can be found at http://www.transtats.bts.gov/ airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") # convert columns to factors airlines["Year"]= airlines["Year"].asfactor() airlines["Month"]= airlines["Month"].asfactor() airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor() airlines["Cancelled"] = airlines["Cancelled"].asfactor() airlines['FlightNum'] = airlines['FlightNum'].asfactor() # set the predictor names and the response column name predictors = ["Origin", "Dest", "Year", "UniqueCarrier", "DayOfWeek", "Month", "Distance", "FlightNum"] response = "IsDepDelayed" # split into train and validation sets train, valid= airlines.split_frame(ratios = [.8], seed = 1234) # try using the `stopping_tolerance` metric: # train your model, where you specify the stopping_metric, stopping_rounds, # and stopping_tolerance # initialize the estimator then train the model airlines_gbm = H2OGradientBoostingEstimator(stopping_metric = "auc", stopping_rounds = 3, stopping_tolerance = 1e-2, seed =1234) airlines_gbm.train(x = predictors, y = response, training_frame = train, validation_frame = valid) # print the auc for the validation data airlines_gbm.auc(valid=True)