# interactions¶

• Available in: GLM, CoxPH
• Hyperparameter: no

## Description¶

By default, interactions between predictor columns are expanded and computed on the fly as GLM iterates over dataset. The interactions option allows you to enter a list of predictor column indices that should interact.

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.

An interactions specification of the form ‘first + second’ computes all of the terms in ‘first’ together with all the terms in ‘second’ with any duplicates removed.

An interactions specification of the form ‘first:second’ indicates the the set of terms obtained by taking the interactions of all terms in ‘first’ with all terms in ‘second’.

An interactions specification ‘first*second’ indicates the cross of ‘first’ and ‘second’. This is the same as ‘first + second + first:second’. The terms in the formula will be re-ordered so that main effects come first followed by the interactions, then all second-order, all third-order and so on.

Interactions can be specified between two categorical columns, between two numeric columns, or between a mix of categorical and numerical columns. When entered, all pairwise combinations of predictor column indices will be computed for that list.

## Examples¶

library(h2o)
h2o.init()
# import the boston dataset:
# this dataset looks at features of the boston suburbs and predicts median housing prices
# the original dataset can be found at https://archive.ics.uci.edu/ml/datasets/Housing
boston <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")

# set the predictor names and the response column name
predictors <- colnames(boston)[1:13]
# set the response column to "medv", the median value of owner-occupied homes in $1000's response <- "medv" # convert the chas column to a factor (chas = Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)) boston["chas"] <- as.factor(boston["chas"]) # split into train and validation sets boston.splits <- h2o.splitFrame(data = boston, ratios = .8) train <- boston.splits[[1]] valid <- boston.splits[[2]] # try using the interactions parameter: # add the interaction terms between 'crim' and 'dis' (per capita crime rate by town and # the weighted distances to five Boston employment centres) # initialize the estimator then train the model interactions_list = c('crim', 'dis') boston_glm <- h2o.glm(x = predictors, y = response, training_frame = train, interactions = interactions_list, validation_frame = valid) # print the mse for validation set print(h2o.mse(boston_glm, valid=TRUE))  import h2o from h2o.estimators.glm import H2OGeneralizedLinearEstimator h2o.init() # import the boston dataset: # this dataset looks at features of the boston suburbs and predicts median housing prices # the original dataset can be found at https://archive.ics.uci.edu/ml/datasets/Housing boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv") # set the predictor names and the response column name predictors = boston.columns[:-1] # set the response column to "medv", the median value of owner-occupied homes in$1000's
response = "medv"

# convert the chas column to a factor (chas = Charles River dummy variable (= 1 if tract bounds river; 0 otherwise))
boston['chas'] = boston['chas'].asfactor()

# split into train and validation sets
train, valid = boston.split_frame(ratios = [.8])

# take a look at the boston columns:
print(boston.columns)

# try using the interactions parameter:
# add the interaction terms between 'crim' and 'dis' (per capita crime rate by town and
# the weighted distances to five Boston employment centres)
# initialize the estimator then train the model
interactions_list = ['crim', 'dis']
boston_glm = H2OGeneralizedLinearEstimator(interactions = interactions_list)
boston_glm.train(x = predictors, y = response, training_frame = train, validation_frame = valid)

# print the mse for the validation data
print(boston_glm.mse(valid=True))