Nothing
Code
translate_args(basic_class %>% set_engine("xgboost"))
Output
$x
missing_arg()
$y
missing_arg()
$weights
missing_arg()
$nthread
[1] 1
$verbose
[1] 0
Code
translate_args(basic_class %>% set_engine("C5.0"))
Output
$x
missing_arg()
$y
missing_arg()
$weights
missing_arg()
Code
translate_args(basic_class %>% set_engine("C5.0", rules = TRUE))
Output
$x
missing_arg()
$y
missing_arg()
$weights
missing_arg()
$rules
<quosure>
expr: ^TRUE
env: empty
Code
translate_args(basic_reg %>% set_engine("xgboost", print_every_n = 10L))
Output
$x
missing_arg()
$y
missing_arg()
$weights
missing_arg()
$print_every_n
<quosure>
expr: ^10L
env: empty
$nthread
[1] 1
$verbose
[1] 0
Code
translate_args(trees %>% set_engine("C5.0"))
Output
$x
missing_arg()
$y
missing_arg()
$weights
missing_arg()
$trials
<quosure>
expr: ^15
env: empty
Code
translate_args(trees %>% set_engine("xgboost"))
Output
$x
missing_arg()
$y
missing_arg()
$weights
missing_arg()
$nrounds
<quosure>
expr: ^15
env: empty
$nthread
[1] 1
$verbose
[1] 0
Code
translate_args(split_num %>% set_engine("C5.0"))
Output
$x
missing_arg()
$y
missing_arg()
$weights
missing_arg()
$minCases
<quosure>
expr: ^15
env: empty
Code
translate_args(split_num %>% set_engine("xgboost"))
Output
$x
missing_arg()
$y
missing_arg()
$weights
missing_arg()
$min_child_weight
<quosure>
expr: ^15
env: empty
$nthread
[1] 1
$verbose
[1] 0
Code
translate_args(basic_class %>% set_engine("rpart"))
Output
$formula
missing_arg()
$data
missing_arg()
$weights
missing_arg()
Code
translate_args(basic_class %>% set_engine("C5.0"))
Output
$x
missing_arg()
$y
missing_arg()
$weights
missing_arg()
$trials
[1] 1
Code
translate_args(basic_class %>% set_engine("C5.0", rules = TRUE))
Output
$x
missing_arg()
$y
missing_arg()
$weights
missing_arg()
$rules
<quosure>
expr: ^TRUE
env: empty
$trials
[1] 1
Code
translate_args(basic_reg %>% set_engine("rpart", model = TRUE))
Output
$formula
missing_arg()
$data
missing_arg()
$weights
missing_arg()
$model
<quosure>
expr: ^TRUE
env: empty
Code
translate_args(cost_complexity %>% set_engine("rpart"))
Output
$formula
missing_arg()
$data
missing_arg()
$weights
missing_arg()
$cp
<quosure>
expr: ^15
env: empty
Code
translate_args(split_num %>% set_engine("C5.0"))
Output
$x
missing_arg()
$y
missing_arg()
$weights
missing_arg()
$minCases
<quosure>
expr: ^15
env: empty
$trials
[1] 1
Code
translate_args(split_num %>% set_engine("rpart"))
Output
$formula
missing_arg()
$data
missing_arg()
$weights
missing_arg()
$minsplit
min_rows(15, data)
Code
translate_args(basic %>% set_engine("parsnip"))
Output
$x
missing_arg()
$y
missing_arg()
Code
translate_args(basic %>% set_engine("parsnip", keepxy = FALSE))
Output
$x
missing_arg()
$y
missing_arg()
$keepxy
<quosure>
expr: ^FALSE
env: empty
Code
translate_args(basic %>% set_engine("lm"))
Output
$formula
missing_arg()
$data
missing_arg()
$weights
missing_arg()
Code
translate_args(basic %>% set_engine("lm", model = FALSE))
Output
$formula
missing_arg()
$data
missing_arg()
$weights
missing_arg()
$model
<quosure>
expr: ^FALSE
env: empty
Code
translate_args(basic %>% set_engine("glm"))
Output
$formula
missing_arg()
$data
missing_arg()
$weights
missing_arg()
$family
stats::gaussian
Code
translate_args(basic %>% set_engine("glm", family = "quasipoisson"))
Output
$formula
missing_arg()
$data
missing_arg()
$weights
missing_arg()
$family
<quosure>
expr: ^"quasipoisson"
env: empty
Code
translate_args(basic %>% set_engine("stan"))
Output
$formula
missing_arg()
$data
missing_arg()
$weights
missing_arg()
$family
stats::gaussian
$refresh
[1] 0
Code
translate_args(basic %>% set_engine("stan", chains = 1, iter = 5))
Output
$formula
missing_arg()
$data
missing_arg()
$weights
missing_arg()
$chains
<quosure>
expr: ^1
env: empty
$iter
<quosure>
expr: ^5
env: empty
$family
stats::gaussian
$refresh
[1] 0
Code
translate_args(basic %>% set_engine("spark"))
Output
$x
missing_arg()
$formula
missing_arg()
$weights
missing_arg()
Code
translate_args(basic %>% set_engine("spark", max_iter = 20))
Output
$x
missing_arg()
$formula
missing_arg()
$weights
missing_arg()
$max_iter
<quosure>
expr: ^20
env: empty
Code
translate_args(basic %>% set_engine("glmnet"))
Condition
Error in `.check_glmnet_penalty_fit()`:
! For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
* There are 0 values for `penalty`.
* To try multiple values for total regularization, use the tune package.
* To predict multiple penalties, use `multi_predict()`
Code
translate_args(mixture %>% set_engine("spark"))
Output
$x
missing_arg()
$formula
missing_arg()
$weights
missing_arg()
$elastic_net_param
<quosure>
expr: ^0.128
env: empty
Code
translate_args(mixture_v %>% set_engine("spark"))
Output
$x
missing_arg()
$formula
missing_arg()
$weights
missing_arg()
$elastic_net_param
<quosure>
expr: ^tune()
env: empty
Code
translate_args(mixture %>% set_engine("glmnet"))
Condition
Error in `.check_glmnet_penalty_fit()`:
! For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
* There are 0 values for `penalty`.
* To try multiple values for total regularization, use the tune package.
* To predict multiple penalties, use `multi_predict()`
Code
translate_args(penalty %>% set_engine("glmnet"))
Output
$x
missing_arg()
$y
missing_arg()
$weights
missing_arg()
$family
[1] "gaussian"
Code
translate_args(penalty %>% set_engine("glmnet", nlambda = 10))
Output
$x
missing_arg()
$y
missing_arg()
$weights
missing_arg()
$nlambda
<quosure>
expr: ^10
env: empty
$family
[1] "gaussian"
Code
translate_args(penalty %>% set_engine("glmnet", path_values = 4:2))
Output
$x
missing_arg()
$y
missing_arg()
$weights
missing_arg()
$lambda
<quosure>
expr: ^4:2
env: empty
$family
[1] "gaussian"
Code
translate_args(penalty %>% set_engine("spark"))
Output
$x
missing_arg()
$formula
missing_arg()
$weights
missing_arg()
$reg_param
<quosure>
expr: ^1
env: empty
Code
translate_args(basic %>% set_engine("glm"))
Output
$formula
missing_arg()
$data
missing_arg()
$weights
missing_arg()
$family
stats::binomial
Code
translate_args(basic %>% set_engine("glm", family = binomial(link = "probit")))
Output
$formula
missing_arg()
$data
missing_arg()
$weights
missing_arg()
$family
<quosure>
expr: ^binomial(link = "probit")
env: empty
Code
translate_args(basic %>% set_engine("glmnet"))
Condition
Error in `.check_glmnet_penalty_fit()`:
! For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
* There are 0 values for `penalty`.
* To try multiple values for total regularization, use the tune package.
* To predict multiple penalties, use `multi_predict()`
Code
translate_args(basic %>% set_engine("LiblineaR"))
Output
$x
missing_arg()
$y
missing_arg()
$verbose
[1] FALSE
Code
translate_args(basic %>% set_engine("LiblineaR", bias = 0))
Output
$x
missing_arg()
$y
missing_arg()
$bias
<quosure>
expr: ^0
env: empty
$verbose
[1] FALSE
Code
translate_args(basic %>% set_engine("stan"))
Output
$formula
missing_arg()
$data
missing_arg()
$weights
missing_arg()
$family
stats::binomial
$refresh
[1] 0
Code
translate_args(basic %>% set_engine("stan", chains = 1, iter = 5))
Output
$formula
missing_arg()
$data
missing_arg()
$weights
missing_arg()
$chains
<quosure>
expr: ^1
env: empty
$iter
<quosure>
expr: ^5
env: empty
$family
stats::binomial
$refresh
[1] 0
Code
translate_args(basic %>% set_engine("spark"))
Output
$x
missing_arg()
$formula
missing_arg()
$weights
missing_arg()
$family
[1] "binomial"
Code
translate_args(basic %>% set_engine("spark", max_iter = 20))
Output
$x
missing_arg()
$formula
missing_arg()
$weights
missing_arg()
$max_iter
<quosure>
expr: ^20
env: empty
$family
[1] "binomial"
Code
translate_args(mixture %>% set_engine("glmnet"))
Condition
Error in `.check_glmnet_penalty_fit()`:
! For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
* There are 0 values for `penalty`.
* To try multiple values for total regularization, use the tune package.
* To predict multiple penalties, use `multi_predict()`
Code
translate_args(mixture %>% set_engine("spark"))
Output
$x
missing_arg()
$formula
missing_arg()
$weights
missing_arg()
$elastic_net_param
<quosure>
expr: ^0.128
env: empty
$family
[1] "binomial"
Code
translate_args(penalty %>% set_engine("glmnet"))
Output
$x
missing_arg()
$y
missing_arg()
$weights
missing_arg()
$family
[1] "binomial"
Code
translate_args(penalty %>% set_engine("glmnet", nlambda = 10))
Output
$x
missing_arg()
$y
missing_arg()
$weights
missing_arg()
$nlambda
<quosure>
expr: ^10
env: empty
$family
[1] "binomial"
Code
translate_args(penalty %>% set_engine("glmnet", path_values = 4:2))
Output
$x
missing_arg()
$y
missing_arg()
$weights
missing_arg()
$lambda
<quosure>
expr: ^4:2
env: empty
$family
[1] "binomial"
Code
translate_args(penalty %>% set_engine("LiblineaR"))
Output
$x
missing_arg()
$y
missing_arg()
$cost
<quosure>
expr: ^1
env: empty
$verbose
[1] FALSE
Code
translate_args(penalty %>% set_engine("spark"))
Output
$x
missing_arg()
$formula
missing_arg()
$weights
missing_arg()
$reg_param
<quosure>
expr: ^1
env: empty
$family
[1] "binomial"
Code
translate_args(mixture_v %>% set_engine("glmnet"))
Condition
Error in `.check_glmnet_penalty_fit()`:
! For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
* There are 0 values for `penalty`.
* To try multiple values for total regularization, use the tune package.
* To predict multiple penalties, use `multi_predict()`
Code
translate_args(mixture_v %>% set_engine("LiblineaR"))
Output
$x
missing_arg()
$y
missing_arg()
$type
<quosure>
expr: ^tune()
env: empty
$verbose
[1] FALSE
Code
translate_args(mixture_v %>% set_engine("spark"))
Output
$x
missing_arg()
$formula
missing_arg()
$weights
missing_arg()
$elastic_net_param
<quosure>
expr: ^tune()
env: empty
$family
[1] "binomial"
Code
translate_args(basic %>% set_engine("earth"))
Output
$formula
missing_arg()
$data
missing_arg()
$weights
missing_arg()
$keepxy
[1] TRUE
Code
translate_args(basic %>% set_engine("earth", keepxy = FALSE))
Output
$formula
missing_arg()
$data
missing_arg()
$weights
missing_arg()
$keepxy
<quosure>
expr: ^FALSE
env: empty
Code
translate_args(num_terms %>% set_engine("earth"))
Output
$formula
missing_arg()
$data
missing_arg()
$weights
missing_arg()
$nprune
<quosure>
expr: ^4
env: empty
$glm
<quosure>
expr: ^list(family = stats::binomial)
env: empty
$keepxy
[1] TRUE
Code
translate_args(prod_degree %>% set_engine("earth"))
Output
$formula
missing_arg()
$data
missing_arg()
$weights
missing_arg()
$degree
<quosure>
expr: ^1
env: empty
$keepxy
[1] TRUE
Code
translate_args(prune_method_v %>% set_engine("earth"))
Output
$formula
missing_arg()
$data
missing_arg()
$weights
missing_arg()
$pmethod
<quosure>
expr: ^tune()
env: empty
$keepxy
[1] TRUE
Code
translate_args(hidden_units %>% set_engine("nnet"))
Output
$formula
missing_arg()
$data
missing_arg()
$size
<quosure>
expr: ^4
env: empty
$trace
[1] FALSE
$linout
[1] TRUE
Code
translate_args(hidden_units %>% set_engine("keras"))
Output
$x
missing_arg()
$y
missing_arg()
$hidden_units
<quosure>
expr: ^4
env: empty
Code
translate_args(no_hidden_units %>% set_engine("nnet"))
Output
$formula
missing_arg()
$data
missing_arg()
$size
[1] 5
$trace
[1] FALSE
$linout
[1] TRUE
Code
translate_args(no_hidden_units %>% set_engine("nnet", abstol = tune()))
Output
$formula
missing_arg()
$data
missing_arg()
$size
[1] 5
$abstol
<quosure>
expr: ^tune()
env: empty
$trace
[1] FALSE
$linout
[1] TRUE
Code
translate_args(no_hidden_units %>% set_engine("keras", validation_split = 0.2))
Output
$x
missing_arg()
$y
missing_arg()
$validation_split
<quosure>
expr: ^0.2
env: empty
Code
translate_args(hess %>% set_engine("nnet", Hess = TRUE))
Output
$formula
missing_arg()
$data
missing_arg()
$size
[1] 5
$Hess
<quosure>
expr: ^TRUE
env: empty
$trace
[1] FALSE
$linout
[1] FALSE
Code
translate_args(all_args %>% set_engine("nnet"))
Output
$formula
missing_arg()
$data
missing_arg()
$size
<quosure>
expr: ^4
env: empty
$decay
<quosure>
expr: ^1e-04
env: empty
$maxit
<quosure>
expr: ^2
env: empty
$trace
[1] FALSE
$linout
[1] FALSE
Code
translate_args(all_args %>% set_engine("keras"))
Output
$x
missing_arg()
$y
missing_arg()
$hidden_units
<quosure>
expr: ^4
env: empty
$penalty
<quosure>
expr: ^1e-04
env: empty
$dropout
<quosure>
expr: ^0
env: empty
$epochs
<quosure>
expr: ^2
env: empty
$activation
<quosure>
expr: ^"softmax"
env: empty
Code
translate_args(basic %>% set_engine("glmnet"))
Condition
Error in `.check_glmnet_penalty_fit()`:
! For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
* There are 0 values for `penalty`.
* To try multiple values for total regularization, use the tune package.
* To predict multiple penalties, use `multi_predict()`
Code
translate_args(mixture %>% set_engine("glmnet"))
Output
$x
missing_arg()
$y
missing_arg()
$weights
missing_arg()
$alpha
<quosure>
expr: ^0.128
env: empty
$family
[1] "multinomial"
Code
translate_args(penalty %>% set_engine("glmnet"))
Output
$x
missing_arg()
$y
missing_arg()
$weights
missing_arg()
$family
[1] "multinomial"
Code
translate_args(penalty %>% set_engine("glmnet", path_values = 4:2))
Output
$x
missing_arg()
$y
missing_arg()
$weights
missing_arg()
$lambda
<quosure>
expr: ^4:2
env: empty
$family
[1] "multinomial"
Code
translate_args(penalty %>% set_engine("glmnet", nlambda = 10))
Output
$x
missing_arg()
$y
missing_arg()
$weights
missing_arg()
$nlambda
<quosure>
expr: ^10
env: empty
$family
[1] "multinomial"
Code
translate_args(mixture_v %>% set_engine("glmnet"))
Output
$x
missing_arg()
$y
missing_arg()
$weights
missing_arg()
$alpha
<quosure>
expr: ^tune()
env: empty
$family
[1] "multinomial"
Code
translate_args(basic %>% set_engine("kknn"))
Output
$formula
missing_arg()
$data
missing_arg()
$ks
min_rows(5, data, 5)
Code
translate_args(neighbors %>% set_engine("kknn"))
Output
$formula
missing_arg()
$data
missing_arg()
$ks
min_rows(2, data, 5)
Code
translate_args(neighbors %>% set_engine("kknn", scale = FALSE))
Output
$formula
missing_arg()
$data
missing_arg()
$ks
min_rows(2, data, 5)
$scale
<quosure>
expr: ^FALSE
env: empty
Code
translate_args(weight_func %>% set_engine("kknn"))
Output
$formula
missing_arg()
$data
missing_arg()
$kernel
<quosure>
expr: ^"triangular"
env: empty
$ks
min_rows(5, data, 5)
Code
translate_args(dist_power %>% set_engine("kknn"))
Output
$formula
missing_arg()
$data
missing_arg()
$distance
<quosure>
expr: ^2
env: empty
$ks
min_rows(5, data, 5)
Code
basic %>% translate_args()
Output
list()
Code
basic_incomplete %>% translate_args()
Condition
Error in `.check_glmnet_penalty_fit()`:
! For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
* There are 0 values for `penalty`.
* To try multiple values for total regularization, use the tune package.
* To predict multiple penalties, use `multi_predict()`
Code
translate_args(basic %>% set_engine("randomForest", norm.votes = FALSE))
Output
$x
missing_arg()
$y
missing_arg()
$norm.votes
<quosure>
expr: ^FALSE
env: empty
Code
translate_args(basic %>% set_engine("spark", min_info_gain = 2))
Output
$x
missing_arg()
$formula
missing_arg()
$type
[1] "regression"
$min_info_gain
<quosure>
expr: ^2
env: empty
$seed
sample.int(10^5, 1)
Code
translate_args(mtry %>% set_engine("ranger"))
Output
$x
missing_arg()
$y
missing_arg()
$weights
missing_arg()
$mtry
min_cols(~4, x)
$num.threads
[1] 1
$verbose
[1] FALSE
$seed
sample.int(10^5, 1)
Code
translate_args(mtry %>% set_engine("randomForest"))
Output
$x
missing_arg()
$y
missing_arg()
$mtry
min_cols(~4, x)
Code
translate_args(mtry %>% set_engine("spark"))
Output
$x
missing_arg()
$formula
missing_arg()
$type
[1] "regression"
$feature_subset_strategy
[1] "4"
$seed
sample.int(10^5, 1)
Code
translate_args(trees %>% set_engine("ranger"))
Output
$x
missing_arg()
$y
missing_arg()
$weights
missing_arg()
$num.trees
<quosure>
expr: ^1000
env: empty
$num.threads
[1] 1
$verbose
[1] FALSE
$seed
sample.int(10^5, 1)
$probability
[1] TRUE
Code
translate_args(trees %>% set_engine("ranger", importance = "impurity"))
Output
$x
missing_arg()
$y
missing_arg()
$weights
missing_arg()
$num.trees
<quosure>
expr: ^1000
env: empty
$importance
<quosure>
expr: ^"impurity"
env: empty
$num.threads
[1] 1
$verbose
[1] FALSE
$seed
sample.int(10^5, 1)
$probability
[1] TRUE
Code
translate_args(trees %>% set_engine("randomForest"))
Output
$x
missing_arg()
$y
missing_arg()
$ntree
<quosure>
expr: ^1000
env: empty
Code
translate_args(trees %>% set_engine("spark"))
Output
$x
missing_arg()
$formula
missing_arg()
$type
[1] "classification"
$num_trees
<quosure>
expr: ^1000
env: empty
$seed
sample.int(10^5, 1)
Code
translate_args(min_n %>% set_engine("ranger"))
Output
$x
missing_arg()
$y
missing_arg()
$weights
missing_arg()
$min.node.size
min_rows(~5, x)
$num.threads
[1] 1
$verbose
[1] FALSE
$seed
sample.int(10^5, 1)
Code
translate_args(min_n %>% set_engine("randomForest"))
Output
$x
missing_arg()
$y
missing_arg()
$nodesize
min_rows(~5, x)
Code
translate_args(min_n %>% set_engine("spark"))
Output
$x
missing_arg()
$formula
missing_arg()
$type
[1] "regression"
$min_instances_per_node
min_rows(~5, x)
$seed
sample.int(10^5, 1)
Code
translate_args(basic %>% set_engine("flexsurv"))
Output
$formula
missing_arg()
$data
missing_arg()
$weights
missing_arg()
Code
translate_args(basic %>% set_engine("flexsurv", cl = 0.99))
Output
$formula
missing_arg()
$data
missing_arg()
$weights
missing_arg()
$cl
<quosure>
expr: ^0.99
env: empty
Code
translate_args(normal %>% set_engine("flexsurv"))
Output
$formula
missing_arg()
$data
missing_arg()
$weights
missing_arg()
$dist
<quosure>
expr: ^"lnorm"
env: empty
Code
translate_args(dist_v %>% set_engine("flexsurv"))
Output
$formula
missing_arg()
$data
missing_arg()
$weights
missing_arg()
$dist
<quosure>
expr: ^tune()
env: empty
Code
basic %>% translate_args()
Output
list()
Code
translate_args(basic %>% set_engine("LiblineaR"))
Output
$x
missing_arg()
$y
missing_arg()
$type
[1] 11
$svr_eps
[1] 0.1
Code
translate_args(basic %>% set_engine("LiblineaR", type = 12))
Output
$x
missing_arg()
$y
missing_arg()
$type
<quosure>
expr: ^12
env: empty
$svr_eps
[1] 0.1
Code
translate_args(basic %>% set_engine("kernlab"))
Output
$x
missing_arg()
$data
missing_arg()
$kernel
[1] "vanilladot"
Code
translate_args(basic %>% set_engine("kernlab", cross = 10))
Output
$x
missing_arg()
$data
missing_arg()
$cross
<quosure>
expr: ^10
env: empty
$kernel
[1] "vanilladot"
Code
translate_args(basic %>% set_engine("kernlab"))
Output
$x
missing_arg()
$data
missing_arg()
$kernel
[1] "polydot"
Code
translate_args(basic %>% set_engine("kernlab", cross = 10))
Output
$x
missing_arg()
$data
missing_arg()
$cross
<quosure>
expr: ^10
env: empty
$kernel
[1] "polydot"
Code
translate_args(degree %>% set_engine("kernlab"))
Output
$x
missing_arg()
$data
missing_arg()
$kernel
[1] "polydot"
$kpar
list(degree = ~2)
Code
translate_args(degree_scale %>% set_engine("kernlab"))
Output
$x
missing_arg()
$data
missing_arg()
$kernel
[1] "polydot"
$kpar
list(degree = ~2, scale = ~1.2)
Code
translate_args(basic %>% set_engine("kernlab"))
Output
$x
missing_arg()
$data
missing_arg()
$kernel
[1] "rbfdot"
Code
translate_args(basic %>% set_engine("kernlab", cross = 10))
Output
$x
missing_arg()
$data
missing_arg()
$cross
<quosure>
expr: ^10
env: empty
$kernel
[1] "rbfdot"
Code
translate_args(rbf_sigma %>% set_engine("kernlab"))
Output
$x
missing_arg()
$data
missing_arg()
$kernel
[1] "rbfdot"
$kpar
list(sigma = ~0.2)
Code
.model_param_name_key(mod)
Output
# A tibble: 2 x 3
user parsnip engine
<chr> <chr> <chr>
1 number of trees trees nrounds
2 min_n min_n min_child_weight
Code
.model_param_name_key(mod, as_tibble = FALSE)
Output
$user_to_parsnip
trees min_n
"number of trees" "min_n"
$parsnip_to_engine
nrounds min_child_weight
"trees" "min_n"
Code
.model_param_name_key(linear_reg())
Output
# A tibble: 0 x 3
# i 3 variables: user <chr>, parsnip <chr>, engine <chr>
Code
.model_param_name_key(linear_reg(), as_tibble = FALSE)
Output
$user_to_parsnip
named character(0)
$parsnip_to_engine
named character(0)
'object' should be a model specification or workflow.
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