eps_sdev¶

library(h2o)
h2o.init()

# import the cars dataset
cars <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")

# Specify model-building exercise (1:binomial, 2:multinomial)
problem <- sample(1:2,1)

# Specify response column based on predictor value and problem type
predictors <- c("displacement","power","weight","acceleration","year")
if ( problem == 1 ) { response_col <- "economy_20mpg"} else { response_col <- "cylinders" }

# Convert the response column to a factor
cars[,response_col] <- as.factor(cars[,response_col])

# Specify model parameters
laplace <- c(1)
min_sdev <- c(0.1)
eps_sdev <- c(0.5)

# Build the model
cars_naivebayes <- h2o.naiveBayes(x=predictors, y=response_col, training_frame=cars,
                                  eps_sdev=eps_sdev, min_sdev=min_sdev, laplace=laplace)
print(cars_naivebayes)

# Specify grid search parameters
grid_space <- list()
grid_space$laplace <- c(1,2)
grid_space$min_sdev <- c(0.1,0.2)
grid_space$eps_sdev <- c(0.5,0.6)

# Specify response column based on predictor value and problem type
predictors <- c("displacement","power","weight","acceleration","year")
if ( problem == 1 ) { response_col <- "economy_20mpg"} else { response_col <- "cylinders" }

# Convert the response column to a factor
cars[,response_col] <- as.factor(cars[,response_col])

# Construct the grid of naive bayes models
cars_naivebayes_grid <- h2o.grid("naivebayes", grid_id="naiveBayes_grid_cars_test",
                                 x=predictors, y=response_col, training_frame=cars,
                                 hyper_params=grid_space)
print(cars_naivebayes_grid)