R/models.R
h2o.feature_interaction.Rd
Metrics: Gain - Total gain of each feature or feature interaction. FScore - Amount of possible splits taken on a feature or feature interaction. wFScore - Amount of possible splits taken on a feature or feature interaction weighed by the probability of the splits to take place. Average wFScore - wFScore divided by FScore. Average Gain - Gain divided by FScore. Expected Gain - Total gain of each feature or feature interaction weighed by the probability to gather the gain. Average Tree Index Average Tree Depth
h2o.feature_interaction( model, max_interaction_depth = 100, max_tree_depth = 100, max_deepening = -1 )
model | A trained xgboost model. |
---|---|
max_interaction_depth | Upper bound for extracted feature interactions depth. Defaults to 100. |
max_tree_depth | Upper bound for tree depth. Defaults to 100. |
max_deepening | Upper bound for interaction start deepening (zero deepening => interactions starting at root only). Defaults to -1. |
# NOT RUN { library(h2o) h2o.init() boston <- h2o.importFile( "https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv", destination_frame="boston" ) boston_xgb <- h2o.xgboost(training_frame = boston, y = "medv", seed = 1234) feature_interactions <- h2o.feature_interaction(boston_xgb) # }