Retrieve the results to view the best predictor subsets.

Retrieve the results to view the best predictor subsets.

h2o.result(model)

Arguments

model	H2OModelSelection object

Value

Returns an H2OFrame object

Examples

if (FALSE) {
library(h2o)
h2o.init()

# Import the prostate dataset:
prostate <- h2o.importFile(
   "http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/prostate.csv"
)

# Set the predictors & response:
predictors <- c("AGE", "RACE", "CAPSULE", "DCAPS", "PSA", "VOL", "DPROS")
response <- "GLEASON"

# Build & train the model:
allsubsetsModel <- h2o.modelSelection(x = predictors,
                                      y = response,
                                      training_frame = prostate,
                                      seed = 12345,
                                      max_predictor_number = 7,
                                      mode = "allsubsets")

# Retrieve the results (H2OFrame containing best model_ids, best_r2_value, & predictor subsets):
results <- h2o.result(allsubsetsModel)
print(results)

# Retrieve the list of coefficients:
coeff <- h2o.coef(allsubsetsModel)
print(coeff)

# Retrieve the list of coefficients for a subset size of 3:
coeff3 <- h2o.coef(allsubsetsModel, 3)
print(coeff3)

# Retrieve the list of standardized coefficients:
coeff_norm <- h2o.coef_norm(allsubsetsModel)
print(coeff_norm)

# Retrieve the list of standardized coefficients for a subset size of 3:
coeff_norm3 <- h2o.coef_norm(allsubsetsModel)
print(coeff_norm3)

# Check the variables that were added during this process:
h2o.get_predictors_added_per_step(allsubsetsModel)

# To find out which variables get removed, build a new model with ``mode = "backward``
# using the above training information:
bwModel <- h2o.modelSelection(x = predictors,
                              y = response,
                              training_frame = prostate,
                              seed = 12345,
                              max_predictor_number = 7,
                              mode = "backward")
h2o.get_predictors_removed_per_step(bwModel)

# To build the fastest model with ModelSelection, use ``mode = "maxrsweep"``:
sweepModel <- h2o.modelSelection(x = predictors,
                                 y = response,
                                 training_frame = prostate,
                                 mode = "maxrsweep",
                                 build_glm_model = FALSE,
                                 max_predictor_number = 3,
                                 seed = 12345)

# Retrieve the results to view the best predictor subsets:
h2o.result(sweepModel)
}