h2o.feature_interaction: Feature interactions and importance, leaf statistics and...

In h2o: R Interface for the 'H2O' Scalable Machine Learning Platform

Feature interactions and importance, leaf statistics and split value histograms in a tabular form. Available for XGBoost and GBM.

Description

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

Usage

h2o.feature_interaction(
  model,
  max_interaction_depth = 100,
  max_tree_depth = 100,
  max_deepening = -1
)

Arguments

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.

Examples

## 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)

## End(Not run)

h2o documentation built on Aug. 9, 2023, 9:06 a.m.