Skip to main content

`interaction_pairs`

  • Available in: GLM, GAM, CoxPH
  • Hyperparameter: no

Description​

By default, interactions between predictor columns are expanded and computed on the fly as GLM iterates over dataset. The interaction_pairs parameter allows you to define a list of specific interactions to include instead of all interactions.

Note that adding a list of interactions to a model changes the interpretation of all of the coefficients. For example, a typical predictor has the form ‘response ~ terms’ where ‘response’ is the (numeric) response vector, and ‘terms’ is a series of terms that specify a linear predictor for ‘response’. For ‘binomial’ and ‘quasibinomial’ families in GLM, the response can also be specified as a ‘factor’ (when the first level denotes failure and all other levels denote success) or as a two-column matrix with the columns giving the numbers of successes and failures.

When using this parameter, specify a list of pairwise columns that should interact. When specified, GLM computes interactions only between the specified pairs rather than between all pairwise combinations.

Note that this option is mutually exclusive with interactions.

Example​

library(h2o)
h2o.init()
# import the airlines dataset
df <-  h2o.importFile("http://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")

# specify the columns to include
XY <- names(df)[c(1, 2, 3, 4, 6, 8, 9, 13, 17, 18, 19, 31)]

# specify the predictor column indices to interact
interactions <- XY[c(5, 7, 9)]

# train the model and build the coefficients table
m1 <- h2o.glm(x = XY[-length(XY)],
             y = XY[length(XY)],
             training_frame = df,
             interactions = interactions, 
             lambda_search = TRUE,
             family = "binomial")
m1_coefs <- m1@model$coefficients_table

# train the model with the interaction pairs
m2 <- h2o.glm(x = XY[-length(XY)],
              y = XY[length(XY)],
              training_frame = df,
              interaction_pairs = list(
               c("CRSDepTime", "UniqueCarrier"),
               c("CRSDepTime", "Origin"),
               c("UniqueCarrier", "Origin")
               ),
              lambda_search = TRUE,
              family = "binomial")
m2_coefs <- m2@model$coefficients_table
Feedback