auuc_nbins

• Available in: Uplift DRF

• Hyperparameter: no

Description

Use this option to specify the number of bins for the calculation of Area Under Uplift Curve (AUUC).

To calculate AUUC for big data, the predictions are binned into histograms. Because of this feature, the results should be different compared to exact computation. To get a value closer to the exact AUUC calculation, a higher number of bins has to be set.

To define AUUC, binned predictions are sorted from largest to the smallest value. For every group, the cumulative sum of observations statistics is calculated. The resulting cumulative uplift is defined based on these statistics.

The default value is -1, which means 1000 bins are used. The value should be higher than zero and less than number of data rows.

Related Parameters

• treatment_column

• response_column

• uplift_metric

• auuc_type

Example

R

Python

library(h2o)
h2o.init()

# Import the uplift dataset into H2O:
data <- h2o.importFile(locate("https://s3.amazonaws.com/h2o-public-test-data/smalldata/uplift/criteo_uplift_13k.csv"))

# Set the predictors, response, and treatment column:
# set the predictors
predictors <- c("f1", "f2", "f3", "f4", "f5", "f6","f7", "f8")
# set the response as a factor
data$conversion <- as.factor(data$conversion)
# set the treatment column as a factor
data$treatment <- as.factor(data$treatment)

# Split the dataset into a train and valid set:
data_split <- h2o.splitFrame(data = data, ratios = 0.8, seed = 1234)
train <- data_split[[1]]
valid <- data_split[[2]]

# Build and train the model:
uplift.model <- h2o.upliftRandomForest(training_frame = train,
                                       validation_frame=valid,
                                       x=predictors,
                                       y="conversion",
                                       ntrees=10,
                                       max_depth=5,
                                       treatment_column="treatment",
                                       uplift_metric="KL",
                                       min_rows=10,
                                       nbins=1000,
                                       seed=1234,
                                       auuc_nbins=1000)
# Eval performance:
perf <- h2o.performance(uplift.model)

# Get AUUC:
auuc <- h2o.auuc(perf)

import h2o
from h2o.estimators import H2OUpliftRandomForestEstimator
h2o.init()

# Import the cars dataset into H2O:
data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/uplift/criteo_uplift_13k.csv")

# Set the predictors, response, and treatment column:
predictors = ["f1", "f2", "f3", "f4", "f5", "f6","f7", "f8"]
# set the response as a factor
response = "conversion"
data[response] = data[response].asfactor()
# set the treatment as a factor
treatment_column = "treatment"
data[treatment_column] = data[treatment_column].asfactor()

# Split the dataset into a train and valid set:
train, valid = data.split_frame(ratios=[.8], seed=1234)

# Build and train the model:
uplift_model = H2OUpliftRandomForestEstimator(ntrees=10,
                                              max_depth=5,
                                              treatment_column=treatment_column,
                                              uplift_metric="KL",
                                              min_rows=10,
                                              nbins=1000,
                                              seed=1234,
                                              auuc_nbins=1000)
uplift_model.train(x=predictors,
                   y=response,
                   training_frame=train,
                   validation_frame=valid)

# Eval performance:
perf = uplift_model.model_performance()

# Get AUUC:
auuc = perf.auuc()