# 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.

## Example¶

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/

# 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))

import h2o
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/

# 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)