`min_sdev`¶

Available in: Naïve-Bayes
Hyperparameter: yes

Description¶

This option specifies the minimum standard deviation to use for observations without enough data.

This option defaults to 0.001 and must be at least 1e-10.

Example¶

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)

import h2o
h2o.init()
import random
from h2o.estimators.naive_bayes import H2ONaiveBayesEstimator
from h2o.grid.grid_search import H2OGridSearch

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

# Specify model-building exercise (1:binomial, 2:multinomial)
problem = random.sample(["binomial","multinomial"],1)

# Specify response column based on predictor value and problem type
predictors = ["displacement","power","weight","acceleration","year"]
if problem == "binomial":
    response_col = "economy_20mpg"
else:
    response_col = "cylinders"

# Convert the response column to a factor
cars[response_col] = cars[response_col].asfactor()

# Train the model
cars_nb = H2ONaiveBayesEstimator(min_sdev=0.1, eps_sdev=0.5, seed=1234)
cars_nb.train(x=predictors, y=response_col, training_frame=cars)
cars_nb.show()

# Predict on training data
cars_pred = cars_nb.predict(cars)
cars_pred.head()

# Specify grid search parameters
hyper_params = {'laplace':[1,2], 'min_sdev':[0.1,0.2], 'eps_sdev':[0.5,0.6]}

# Construct the grid of naive bayes models
cars_nb = H2ONaiveBayesEstimator(seed = 1234)
cars_grid = H2OGridSearch(model=cars_nb, hyper_params=hyper_params)

# Train using the grid
cars_grid.train(x=predictors, y=response_col, training_frame=cars)
cars_grid.show()