parsnip-specs.md
In agua: 'tidymodels' Integration with 'h2o'

random forest specs

Code
  rand_forest(mtry = 2, trees = 1000) %>% set_engine("h2o") %>% set_mode(
    "regression") %>% translate()
Output
  Random Forest Model Specification (regression)

  Main Arguments:
    mtry = 2
    trees = 1000

  Computational engine: h2o

  Model fit template:
  agua::h2o_train_rf(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
      validation_frame = missing_arg(), mtries = 2, ntrees = 1000)

Code
  rand_forest(mtry = 2, trees = 1000) %>% set_engine("h2o", sample_rate = 1 / 3,
  distribution = "quantile") %>% set_mode("regression") %>% translate()
Output
  Random Forest Model Specification (regression)

  Main Arguments:
    mtry = 2
    trees = 1000

  Engine-Specific Arguments:
    sample_rate = 1/3
    distribution = quantile

  Computational engine: h2o

  Model fit template:
  agua::h2o_train_rf(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
      validation_frame = missing_arg(), mtries = 2, ntrees = 1000, 
      sample_rate = 1/3, distribution = "quantile")

xgboost specs

Code
  boost_tree(learn_rate = 0.1, trees = 1000) %>% set_engine("h2o") %>% set_mode(
    "regression") %>% translate()
Output
  Boosted Tree Model Specification (regression)

  Main Arguments:
    trees = 1000
    learn_rate = 0.1

  Computational engine: h2o

  Model fit template:
  agua::h2o_train_xgboost(x = missing_arg(), y = missing_arg(), 
      weights = missing_arg(), validation_frame = missing_arg(), 
      ntrees = 1000, learn_rate = 0.1)

Code
  boost_tree(learn_rate = 0.1, trees = 1000) %>% set_engine("h2o", gamma = 1 / 3,
  validation = 0.1) %>% set_mode("regression") %>% translate()
Output
  Boosted Tree Model Specification (regression)

  Main Arguments:
    trees = 1000
    learn_rate = 0.1

  Engine-Specific Arguments:
    gamma = 1/3
    validation = 0.1

  Computational engine: h2o

  Model fit template:
  agua::h2o_train_xgboost(x = missing_arg(), y = missing_arg(), 
      weights = missing_arg(), validation_frame = missing_arg(), 
      ntrees = 1000, learn_rate = 0.1, gamma = 1/3, validation = 0.1)

gbm specs

Code
  boost_tree(learn_rate = 0.1, trees = 1000) %>% set_engine("h2o_gbm") %>%
    set_mode("regression") %>% translate()
Output
  Boosted Tree Model Specification (regression)

  Main Arguments:
    trees = 1000
    learn_rate = 0.1

  Computational engine: h2o_gbm

  Model fit template:
  agua::h2o_train_gbm(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
      validation_frame = missing_arg(), ntrees = 1000, learn_rate = 0.1)

Code
  boost_tree(learn_rate = 0.1, trees = 1000) %>% set_engine("h2o_gbm", gamma = 1 /
    3, validation = 0.1) %>% set_mode("regression") %>% translate()
Output
  Boosted Tree Model Specification (regression)

  Main Arguments:
    trees = 1000
    learn_rate = 0.1

  Engine-Specific Arguments:
    gamma = 1/3
    validation = 0.1

  Computational engine: h2o_gbm

  Model fit template:
  agua::h2o_train_gbm(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
      validation_frame = missing_arg(), ntrees = 1000, learn_rate = 0.1, 
      gamma = 1/3, validation = 0.1)

linear regression specs

Code
  linear_reg(mixture = 0.5, penalty = 0.01) %>% set_engine("h2o") %>% set_mode(
    "regression") %>% translate()
Output
  Linear Regression Model Specification (regression)

  Main Arguments:
    penalty = 0.01
    mixture = 0.5

  Computational engine: h2o

  Model fit template:
  agua::h2o_train_glm(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
      validation_frame = missing_arg(), lambda = 0.01, alpha = 0.5, 
      family = "gaussian")

Code
  linear_reg(mixture = 0.5, penalty = 0.01) %>% set_engine("h2o", solver = "IRLSM") %>%
    set_mode("regression") %>% translate()
Output
  Linear Regression Model Specification (regression)

  Main Arguments:
    penalty = 0.01
    mixture = 0.5

  Engine-Specific Arguments:
    solver = IRLSM

  Computational engine: h2o

  Model fit template:
  agua::h2o_train_glm(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
      validation_frame = missing_arg(), lambda = 0.01, alpha = 0.5, 
      solver = "IRLSM", family = "gaussian")

logistic regression specs

Code
  logistic_reg(mixture = 0.5, penalty = 0.01) %>% set_engine("h2o") %>% set_mode(
    "classification") %>% translate()
Output
  Logistic Regression Model Specification (classification)

  Main Arguments:
    penalty = 0.01
    mixture = 0.5

  Computational engine: h2o

  Model fit template:
  agua::h2o_train_glm(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
      validation_frame = missing_arg(), lambda = 0.01, alpha = 0.5, 
      family = "binomial")

Code
  logistic_reg(mixture = 0.5, penalty = 0.01) %>% set_engine("h2o", theta = 1e-05) %>%
    set_mode("classification") %>% translate()
Output
  Logistic Regression Model Specification (classification)

  Main Arguments:
    penalty = 0.01
    mixture = 0.5

  Engine-Specific Arguments:
    theta = 1e-05

  Computational engine: h2o

  Model fit template:
  agua::h2o_train_glm(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
      validation_frame = missing_arg(), lambda = 0.01, alpha = 0.5, 
      theta = 1e-05, family = "binomial")

poisson regression specs

Code
  poisson_reg(engine = "h2o", mixture = 0.5, penalty = 0.01) %>% set_engine("h2o") %>%
    set_mode("regression") %>% translate()
Output
  Poisson Regression Model Specification (regression)

  Main Arguments:
    penalty = 0.01
    mixture = 0.5

  Computational engine: h2o

  Model fit template:
  agua::h2o_train_glm(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
      validation_frame = missing_arg(), lambda = 0.01, alpha = 0.5, 
      family = "poisson")

Code
  poisson_reg(engine = "h2o", mixture = 0.5, penalty = 0.01) %>% set_engine("h2o",
    solver = "L_BFGS") %>% set_mode("regression") %>% translate()
Output
  Poisson Regression Model Specification (regression)

  Main Arguments:
    penalty = 0.01
    mixture = 0.5

  Engine-Specific Arguments:
    solver = L_BFGS

  Computational engine: h2o

  Model fit template:
  agua::h2o_train_glm(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
      validation_frame = missing_arg(), lambda = 0.01, alpha = 0.5, 
      solver = "L_BFGS", family = "poisson")

multinomial regression specs

Code
  multinom_reg(mixture = 0.5, penalty = 0.01) %>% set_engine("h2o") %>% set_mode(
    "classification") %>% translate()
Output
  Multinomial Regression Model Specification (classification)

  Main Arguments:
    penalty = 0.01
    mixture = 0.5

  Computational engine: h2o

  Model fit template:
  agua::h2o_train_glm(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
      validation_frame = missing_arg(), lambda = 0.01, alpha = 0.5, 
      family = "multinomial")

Code
  multinom_reg(mixture = 0.5, penalty = 0.01) %>% set_engine("h2o", theta = 1e-05) %>%
    set_mode("classification") %>% translate()
Output
  Multinomial Regression Model Specification (classification)

  Main Arguments:
    penalty = 0.01
    mixture = 0.5

  Engine-Specific Arguments:
    theta = 1e-05

  Computational engine: h2o

  Model fit template:
  agua::h2o_train_glm(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
      validation_frame = missing_arg(), lambda = 0.01, alpha = 0.5, 
      theta = 1e-05, family = "multinomial")

naive bayes specs

Code
  naive_Bayes(engine = "h2o", Laplace = 1) %>% set_mode("classification") %>%
    translate()
Output
  Naive Bayes Model Specification (classification)

  Main Arguments:
    Laplace = 1

  Computational engine: h2o

  Model fit template:
  agua::h2o_train_nb(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
      validation_frame = missing_arg(), laplace = 1)

Code
  naive_Bayes(engine = "h2o", Laplace = 1) %>% set_engine("h2o", min_sdev = 1e-10,
    min_prob = 1e-05) %>% set_mode("classification") %>% translate()
Output
  Naive Bayes Model Specification (classification)

  Main Arguments:
    Laplace = 1

  Engine-Specific Arguments:
    min_sdev = 1e-10
    min_prob = 1e-05

  Computational engine: h2o

  Model fit template:
  agua::h2o_train_nb(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
      validation_frame = missing_arg(), laplace = 1, min_sdev = 1e-10, 
      min_prob = 1e-05)

mlp specs

Code
  mlp(hidden_units = 100, penalty = 0.5, activation = "relu") %>% set_engine(
    "h2o") %>% set_mode("regression") %>% translate()
Output
  Single Layer Neural Network Model Specification (regression)

  Main Arguments:
    hidden_units = 100
    penalty = 0.5
    activation = relu

  Computational engine: h2o

  Model fit template:
  agua::h2o_train_mlp(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
      validation_frame = missing_arg(), hidden = 100, l2 = 0.5, 
      activation = "relu")

Code
  mlp(hidden_units = 100, penalty = 0.5, activation = "relu") %>% set_engine(
    "h2o", standarize = FALSE) %>% set_mode("regression") %>% translate()
Output
  Single Layer Neural Network Model Specification (regression)

  Main Arguments:
    hidden_units = 100
    penalty = 0.5
    activation = relu

  Engine-Specific Arguments:
    standarize = FALSE

  Computational engine: h2o

  Model fit template:
  agua::h2o_train_mlp(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
      validation_frame = missing_arg(), hidden = 100, l2 = 0.5, 
      activation = "relu", standarize = FALSE)

Code
  rule_fit(engine = "h2o", trees = 100, tree_depth = 5) %>% set_mode("regression") %>%
    translate()
Output
  RuleFit Model Specification (regression)

  Main Arguments:
    trees = 100
    tree_depth = 5

  Computational engine: h2o

  Model fit template:
  agua::h2o_train_rule(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
      validation_frame = missing_arg(), rule_generation_ntrees = 100, 
      max_rule_length = 5)

Code
  rule_fit(engine = "h2o", trees = 100, tree_depth = 5) %>% set_engine("h2o",
    algorithm = "DRF") %>% set_mode("regression") %>% translate()
Output
  RuleFit Model Specification (regression)

  Main Arguments:
    trees = 100
    tree_depth = 5

  Engine-Specific Arguments:
    algorithm = DRF

  Computational engine: h2o

  Model fit template:
  agua::h2o_train_rule(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
      validation_frame = missing_arg(), rule_generation_ntrees = 100, 
      max_rule_length = 5, algorithm = "DRF")