tests/testthat/_snaps/registration.md

In topepo/parsnip: A Common API to Modeling and Analysis Functions

adding a new engine

Code
  set_model_engine("sponge", mode = "regression", eng = "gum")
Condition
  Error in `check_mode_for_new_engine()`:
  ! "regression" is not a known mode for model `sponge()`.

showing model info

Code
  show_model_info("rand_forest")
Output
  Information for `rand_forest`
   modes: unknown, classification, regression, censored regression

   engines: 
     classification: randomForest, ranger1, spark
     regression:     randomForest, ranger1, spark

  1The model can use case weights.

   arguments: 
     ranger:       
        mtry  --> mtry
        trees --> num.trees
        min_n --> min.node.size
     randomForest: 
        mtry  --> mtry
        trees --> ntree
        min_n --> nodesize
     spark:        
        mtry  --> feature_subset_strategy
        trees --> num_trees
        min_n --> min_instances_per_node

   fit modules:
           engine           mode
           ranger classification
           ranger     regression
     randomForest classification
     randomForest     regression
            spark classification
            spark     regression

   prediction modules:
               mode       engine                    methods
     classification randomForest           class, prob, raw
     classification       ranger class, conf_int, prob, raw
     classification        spark                class, prob
         regression randomForest               numeric, raw
         regression       ranger     conf_int, numeric, raw
         regression        spark                    numeric

Code
  show_model_info("mlp")
Output
  Information for `mlp`
   modes: unknown, classification, regression

   engines: 
     classification: brulee, keras, nnet
     regression:     brulee, keras, nnet


   arguments: 
     keras:  
        hidden_units --> hidden_units
        penalty      --> penalty
        dropout      --> dropout
        epochs       --> epochs
        activation   --> activation
     nnet:   
        hidden_units --> size
        penalty      --> decay
        epochs       --> maxit
     brulee: 
        hidden_units --> hidden_units
        penalty      --> penalty
        epochs       --> epochs
        dropout      --> dropout
        learn_rate   --> learn_rate
        activation   --> activation

   fit modules:
     engine           mode
      keras     regression
      keras classification
       nnet     regression
       nnet classification
     brulee     regression
     brulee classification

   prediction modules:
               mode engine          methods
     classification brulee      class, prob
     classification  keras class, prob, raw
     classification   nnet class, prob, raw
         regression brulee          numeric
         regression  keras     numeric, raw
         regression   nnet     numeric, raw

topepo/parsnip documentation built on April 16, 2024, 3:23 a.m.