`gainslift_bins`

• Available in: Deeplearning, DRF, GAM, GBM, GLM, Naïve-Bayes, Stacked Ensemble, XGBoost
• Hyperparameter: no

Description

The Kolmogorov-Smirnov (KS) metric represents the degree of separation between the positive and negative distribution functions for a binomial model. Detailed metrics per each group can be found in the Gains/Lift table.

The gainslift_bins option specifies the number of bins for a Gains/Lift table. The default value is -1 and makes the binning automatic. To disable this feature, set to 0.

Related parameters

• None

Example

R

library(h2o)
h2o.init()

# import the airlines dataset:
airlines <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/airlines_train.csv")

# build and train the model:
model <- h2o.gbm(x = c("Origin", "Distance"), 
                 y = "IsDepDelayed", 
                 training_frame = airlines, 
                 ntrees = 1, 
                 gainslift_bins = 20)

# print the Gains/Lift table for the model:
print(h2o.gainsLift(model))

Python

import h2o
from h2o.estimators import H2OGradientBoostingEstimator
h2o.init()

# import the airlines dataset:
airlines = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/airlines_train.csv")

# build and train the model:
model = H2OGradientBoostingEstimator(ntrees=1, gainslift_bins=20)
model.train(x=["Origin", "Distance"], y="IsDepDelayed", training_frame=airlines)

# print the Gains/Lift table for the model:
print(model.gains_lift())

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