# auc_type¶

• Available in: Random Forest, GBM, Deep Learning, XGBoost, GLM, GAM, Naive Bayes, Stack Ensembles

• Hyperparameter: no

## Description¶

To calculate AUC or AUCPR for multinomial classification, this parameter has to be set. By default, this option is disabled due to expensive CPU and memory usage. This functionality is available only for multinomial classification problems with a maximum of 50 domains.

This parameter is important when the early_stopping parameter is set to AUC and also for sorting model by metric in Grid search using sort_by parameter.

• If the AUC type is WEIGHTED_OVR, the weighted average One vs. Rest AUC or AUCPR will be the default value for AUC and AUCPR metrics.

• If the default AUC type is WEIGHTED_OVO, the weighted average One vs. One AUC or AUCPR will be the default value for AUC and AUCPR metrics.

• If the default AUC type is MACRO_OVR, the macro average One vs. Rest AUC or AUCPR will be the default value for AUC and AUCPR metrics.

• If the default AUC type is MACRO_OVO, the macro average One vs. One AUC or AUCPR will be the default value for AUC and AUCPR metrics.

• If the default AUC type is NONE, the metric is not calculated and the None value is returned instead.

• If the default AUC type is AUTO, the auto option is NONE by default.

NOTE: auc_type is available ONLY for multinomial distribution and is NONE by default.

## Example¶

library(h2o)
h2o.init()

# Import the cars dataset:
# (this dataset is used to classify whether or not a car is economical based on
# the car's displacement, power, weight, and acceleration, and the year it was made)
cars <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")

# Set the predictor and response column names:
predictors <- c("displacement", "power", "weight", "acceleration", "year")
response <- "cylinders"
cars[response] <- h2o.asfactor(cars[response])

# Split into train and validation sets:
cars_splits <- h2o.splitFrame(data = cars, ratios = 0.8), seed = 1234)
train <- cars_splits[[1]]
valid <- cars_splits[[2]]

# Try using the distribution parameter & train a GBM:
car_gbm <- h2o.gbm(x = predictors,
y = response,
training_frame = train,
validation_frame = valid,
distribution = "multinomial",
auc_type = "MACRO_OVR",
seed = 1234)

# Print the AUC for your validation data:
print(h2o.auc(car_gbm, valid = TRUE))
# Print the AUCPR for your validation data:
print(h2o.aucpr(car_gbm, valid = TRUE))
# Print the whole AUC table:
print(cars_gbm.multinomial_auc_table())
# Print the whole AUCPR table:
print(cars_gbm.multinomial_aucpr_table())

import h2o
h2o.init()

# Import the cars dataset:
# (this dataset is used to classify whether or not a car is economical based on
# the car's displacement, power, weight, and acceleration, and the year it was made)
cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")

# Set the predictor and responsecolumn names:
predictors - ["displacement","power","weight","acceleration","year"]
response = "cylinders"
cars[response] = cars[response].asfactor()

# Split into train and validation sets:
train, valid = cars.split_frame(ratios = [.8], seed = 1234)

# Try using the distribution parameter & train a GBM:
seed=1234,
auc_type="MACRO_OVR")
cars_gbm.train(x=predictors,
y=response,
training_frame=train,
validation_frame=valid)

# Print the AUC for the validation data:
print(cars_gbm.auc(valid=True))
# Print the AUCPR for the validation data:
print(cars_gbm.pr_auc(valid=True))
# Print the whole AUC table:
print(cars_gbm.multinomial_auc_table())
# Print the whole AUCPR table:
print(cars_gbm.multinomial_aucpr_table())