One row per model per backend: what task it can do, which package provides it, whether that package is installed here, and what its single tunable parameter is.
Details
Models are named, not numbered: fit_ml(..., model = "cart") finds its own
backend, and estimator = is needed only to force a particular one when a
model exists in several (ridge, lasso, elastic, svm, bayes).
Examples
models()
#> MODELS
#> Registered 37
#> Usable here 37
#>
#> model estimator package regr class here tunes
#> -------------------------------------------------------------------------
#> mean native base yes - yes -
#> majority native base - yes yes -
#> linear native base yes - yes -
#> logistic native base - yes yes -
#> ridge native base yes yes yes lambda
#> lasso native base yes yes yes lambda
#> elastic native base yes yes yes lambda
#> pcr native base yes - yes ncomp
#> knn native base yes yes yes k
#> tree native base yes yes yes -
#> boost native base yes yes yes rounds
#> spline native base yes yes yes df
#> lda native base - yes yes -
#> bayes native base - yes yes -
#> loess stats base yes - yes span
#> ppr stats base yes - yes nterms
#> isotonic stats base yes - yes -
#> cart rpart rpart yes yes yes cp
#> mlp nnet nnet yes yes yes size
#> multinom nnet nnet - yes yes decay
#> gam mgcv mgcv yes yes yes k
#> qda MASS MASS - yes yes -
#> ridge glmnet glmnet yes yes yes lambda
#> lasso glmnet glmnet yes yes yes lambda
#> elastic glmnet glmnet yes yes yes lambda
#> forest ranger ranger yes yes yes mtry
#> extratrees ranger ranger yes yes yes mtry
#> svm e1071 e1071 yes yes yes cost
#> bayes e1071 e1071 - yes yes -
#> xgboost xgboost xgboost yes yes yes rounds
#> ksvm kernlab kernlab yes yes yes cost
#> gp kernlab kernlab yes yes yes -
#> pls pls pls yes - yes ncomp
#> ctree partykit partykit yes yes yes alpha
#> quantile quantreg quantreg yes - yes -
#> rf randomForest randomForest yes yes yes mtry
#> glmboost mboost mboost yes yes yes rounds
#>
#> model = names above are enough; estimator = only to disambiguate
models(task = "classification", available = TRUE)
#> MODELS
#> Registered 29
#> Usable here 29
#>
#> model estimator package regr class here tunes
#> -------------------------------------------------------------------------
#> majority native base - yes yes -
#> logistic native base - yes yes -
#> ridge native base yes yes yes lambda
#> lasso native base yes yes yes lambda
#> elastic native base yes yes yes lambda
#> knn native base yes yes yes k
#> tree native base yes yes yes -
#> boost native base yes yes yes rounds
#> spline native base yes yes yes df
#> lda native base - yes yes -
#> bayes native base - yes yes -
#> cart rpart rpart yes yes yes cp
#> mlp nnet nnet yes yes yes size
#> multinom nnet nnet - yes yes decay
#> gam mgcv mgcv yes yes yes k
#> qda MASS MASS - yes yes -
#> ridge glmnet glmnet yes yes yes lambda
#> lasso glmnet glmnet yes yes yes lambda
#> elastic glmnet glmnet yes yes yes lambda
#> forest ranger ranger yes yes yes mtry
#> extratrees ranger ranger yes yes yes mtry
#> svm e1071 e1071 yes yes yes cost
#> bayes e1071 e1071 - yes yes -
#> xgboost xgboost xgboost yes yes yes rounds
#> ksvm kernlab kernlab yes yes yes cost
#> gp kernlab kernlab yes yes yes -
#> ctree partykit partykit yes yes yes alpha
#> rf randomForest randomForest yes yes yes mtry
#> glmboost mboost mboost yes yes yes rounds
#>
#> model = names above are enough; estimator = only to disambiguate